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Prediction of Arabic Legal Rulings using Large Language Models (2310.10260v1)
Published 16 Oct 2023 in cs.CL, cs.AI, and cs.LG
Abstract: In the intricate field of legal studies, the analysis of court decisions is a cornerstone for the effective functioning of the judicial system. The ability to predict court outcomes helps judges during the decision-making process and equips lawyers with invaluable insights, enhancing their strategic approaches to cases. Despite its significance, the domain of Arabic court analysis remains under-explored. This paper pioneers a comprehensive predictive analysis of Arabic court decisions on a dataset of 10,813 commercial court real cases, leveraging the advanced capabilities of the current state-of-the-art LLMs. Through a systematic exploration, we evaluate three prevalent foundational models (LLaMA-7b, JAIS-13b, and GPT3.5-turbo) and three training paradigms: zero-shot, one-shot, and tailored fine-tuning. Besides, we assess the benefit of summarizing and/or translating the original Arabic input texts. This leads to a spectrum of 14 model variants, for which we offer a granular performance assessment with a series of different metrics (human assessment, GPT evaluation, ROUGE, and BLEU scores). We show that all variants of LLaMA models yield limited performance, whereas GPT-3.5-based models outperform all other models by a wide margin, surpassing the average score of the dedicated Arabic-centric JAIS model by 50%. Furthermore, we show that all scores except human evaluation are inconsistent and unreliable for assessing the performance of LLMs on court decision predictions. This study paves the way for future research, bridging the gap between computational linguistics and Arabic legal analytics.
- Attia, M.: Handling arabic morphological and syntactic ambiguity within the lfg framework with a view to machine translation. PhD thesis, University of Manchester (2008) Dai and Le [2015] Dai, A.M., Le, Q.V.: Semi-supervised sequence learning. Advances in neural information processing systems 28 (2015) Howard and Ruder [2018] Howard, J., Ruder, S.: Universal language model fine-tuning for text classification. arXiv preprint arXiv:1801.06146 (2018) Katz et al. [2017] Katz, D.M., Bommarito, M.J., Blackman, J.: A general approach for predicting the behavior of the supreme court of the united states. PloS one 12(4), 0174698 (2017) Vaswani et al. [2017] Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017) Sutskever et al. [2014] Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014) Almusharraf [2022] Almusharraf, M.A.M.I.X.D.N.: Automated and human interaction in written discourse: A contrastive parallel corpus-based investigation of metadiscourse features in machine-human translations. Sage Open (2022) https://doi.org/10.1177/21582440221142210 Maas et al. [2011] Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Dai, A.M., Le, Q.V.: Semi-supervised sequence learning. Advances in neural information processing systems 28 (2015) Howard and Ruder [2018] Howard, J., Ruder, S.: Universal language model fine-tuning for text classification. arXiv preprint arXiv:1801.06146 (2018) Katz et al. [2017] Katz, D.M., Bommarito, M.J., Blackman, J.: A general approach for predicting the behavior of the supreme court of the united states. PloS one 12(4), 0174698 (2017) Vaswani et al. [2017] Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017) Sutskever et al. [2014] Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014) Almusharraf [2022] Almusharraf, M.A.M.I.X.D.N.: Automated and human interaction in written discourse: A contrastive parallel corpus-based investigation of metadiscourse features in machine-human translations. Sage Open (2022) https://doi.org/10.1177/21582440221142210 Maas et al. [2011] Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Howard, J., Ruder, S.: Universal language model fine-tuning for text classification. arXiv preprint arXiv:1801.06146 (2018) Katz et al. [2017] Katz, D.M., Bommarito, M.J., Blackman, J.: A general approach for predicting the behavior of the supreme court of the united states. PloS one 12(4), 0174698 (2017) Vaswani et al. [2017] Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017) Sutskever et al. [2014] Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014) Almusharraf [2022] Almusharraf, M.A.M.I.X.D.N.: Automated and human interaction in written discourse: A contrastive parallel corpus-based investigation of metadiscourse features in machine-human translations. Sage Open (2022) https://doi.org/10.1177/21582440221142210 Maas et al. [2011] Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Katz, D.M., Bommarito, M.J., Blackman, J.: A general approach for predicting the behavior of the supreme court of the united states. PloS one 12(4), 0174698 (2017) Vaswani et al. [2017] Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017) Sutskever et al. [2014] Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014) Almusharraf [2022] Almusharraf, M.A.M.I.X.D.N.: Automated and human interaction in written discourse: A contrastive parallel corpus-based investigation of metadiscourse features in machine-human translations. Sage Open (2022) https://doi.org/10.1177/21582440221142210 Maas et al. [2011] Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017) Sutskever et al. [2014] Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014) Almusharraf [2022] Almusharraf, M.A.M.I.X.D.N.: Automated and human interaction in written discourse: A contrastive parallel corpus-based investigation of metadiscourse features in machine-human translations. Sage Open (2022) https://doi.org/10.1177/21582440221142210 Maas et al. [2011] Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014) Almusharraf [2022] Almusharraf, M.A.M.I.X.D.N.: Automated and human interaction in written discourse: A contrastive parallel corpus-based investigation of metadiscourse features in machine-human translations. Sage Open (2022) https://doi.org/10.1177/21582440221142210 Maas et al. [2011] Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Almusharraf, M.A.M.I.X.D.N.: Automated and human interaction in written discourse: A contrastive parallel corpus-based investigation of metadiscourse features in machine-human translations. Sage Open (2022) https://doi.org/10.1177/21582440221142210 Maas et al. [2011] Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Dai, A.M., Le, Q.V.: Semi-supervised sequence learning. Advances in neural information processing systems 28 (2015) Howard and Ruder [2018] Howard, J., Ruder, S.: Universal language model fine-tuning for text classification. arXiv preprint arXiv:1801.06146 (2018) Katz et al. [2017] Katz, D.M., Bommarito, M.J., Blackman, J.: A general approach for predicting the behavior of the supreme court of the united states. PloS one 12(4), 0174698 (2017) Vaswani et al. [2017] Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017) Sutskever et al. [2014] Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014) Almusharraf [2022] Almusharraf, M.A.M.I.X.D.N.: Automated and human interaction in written discourse: A contrastive parallel corpus-based investigation of metadiscourse features in machine-human translations. Sage Open (2022) https://doi.org/10.1177/21582440221142210 Maas et al. [2011] Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Howard, J., Ruder, S.: Universal language model fine-tuning for text classification. arXiv preprint arXiv:1801.06146 (2018) Katz et al. [2017] Katz, D.M., Bommarito, M.J., Blackman, J.: A general approach for predicting the behavior of the supreme court of the united states. PloS one 12(4), 0174698 (2017) Vaswani et al. [2017] Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017) Sutskever et al. [2014] Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014) Almusharraf [2022] Almusharraf, M.A.M.I.X.D.N.: Automated and human interaction in written discourse: A contrastive parallel corpus-based investigation of metadiscourse features in machine-human translations. Sage Open (2022) https://doi.org/10.1177/21582440221142210 Maas et al. [2011] Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Katz, D.M., Bommarito, M.J., Blackman, J.: A general approach for predicting the behavior of the supreme court of the united states. PloS one 12(4), 0174698 (2017) Vaswani et al. [2017] Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017) Sutskever et al. [2014] Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014) Almusharraf [2022] Almusharraf, M.A.M.I.X.D.N.: Automated and human interaction in written discourse: A contrastive parallel corpus-based investigation of metadiscourse features in machine-human translations. Sage Open (2022) https://doi.org/10.1177/21582440221142210 Maas et al. [2011] Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017) Sutskever et al. [2014] Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014) Almusharraf [2022] Almusharraf, M.A.M.I.X.D.N.: Automated and human interaction in written discourse: A contrastive parallel corpus-based investigation of metadiscourse features in machine-human translations. Sage Open (2022) https://doi.org/10.1177/21582440221142210 Maas et al. [2011] Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014) Almusharraf [2022] Almusharraf, M.A.M.I.X.D.N.: Automated and human interaction in written discourse: A contrastive parallel corpus-based investigation of metadiscourse features in machine-human translations. Sage Open (2022) https://doi.org/10.1177/21582440221142210 Maas et al. [2011] Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Almusharraf, M.A.M.I.X.D.N.: Automated and human interaction in written discourse: A contrastive parallel corpus-based investigation of metadiscourse features in machine-human translations. Sage Open (2022) https://doi.org/10.1177/21582440221142210 Maas et al. [2011] Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Howard, J., Ruder, S.: Universal language model fine-tuning for text classification. arXiv preprint arXiv:1801.06146 (2018) Katz et al. [2017] Katz, D.M., Bommarito, M.J., Blackman, J.: A general approach for predicting the behavior of the supreme court of the united states. PloS one 12(4), 0174698 (2017) Vaswani et al. [2017] Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017) Sutskever et al. [2014] Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014) Almusharraf [2022] Almusharraf, M.A.M.I.X.D.N.: Automated and human interaction in written discourse: A contrastive parallel corpus-based investigation of metadiscourse features in machine-human translations. Sage Open (2022) https://doi.org/10.1177/21582440221142210 Maas et al. [2011] Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Katz, D.M., Bommarito, M.J., Blackman, J.: A general approach for predicting the behavior of the supreme court of the united states. PloS one 12(4), 0174698 (2017) Vaswani et al. [2017] Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017) Sutskever et al. [2014] Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014) Almusharraf [2022] Almusharraf, M.A.M.I.X.D.N.: Automated and human interaction in written discourse: A contrastive parallel corpus-based investigation of metadiscourse features in machine-human translations. Sage Open (2022) https://doi.org/10.1177/21582440221142210 Maas et al. [2011] Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017) Sutskever et al. [2014] Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014) Almusharraf [2022] Almusharraf, M.A.M.I.X.D.N.: Automated and human interaction in written discourse: A contrastive parallel corpus-based investigation of metadiscourse features in machine-human translations. Sage Open (2022) https://doi.org/10.1177/21582440221142210 Maas et al. [2011] Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014) Almusharraf [2022] Almusharraf, M.A.M.I.X.D.N.: Automated and human interaction in written discourse: A contrastive parallel corpus-based investigation of metadiscourse features in machine-human translations. Sage Open (2022) https://doi.org/10.1177/21582440221142210 Maas et al. [2011] Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Almusharraf, M.A.M.I.X.D.N.: Automated and human interaction in written discourse: A contrastive parallel corpus-based investigation of metadiscourse features in machine-human translations. Sage Open (2022) https://doi.org/10.1177/21582440221142210 Maas et al. [2011] Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Katz, D.M., Bommarito, M.J., Blackman, J.: A general approach for predicting the behavior of the supreme court of the united states. PloS one 12(4), 0174698 (2017) Vaswani et al. [2017] Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017) Sutskever et al. [2014] Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014) Almusharraf [2022] Almusharraf, M.A.M.I.X.D.N.: Automated and human interaction in written discourse: A contrastive parallel corpus-based investigation of metadiscourse features in machine-human translations. Sage Open (2022) https://doi.org/10.1177/21582440221142210 Maas et al. [2011] Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017) Sutskever et al. [2014] Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014) Almusharraf [2022] Almusharraf, M.A.M.I.X.D.N.: Automated and human interaction in written discourse: A contrastive parallel corpus-based investigation of metadiscourse features in machine-human translations. Sage Open (2022) https://doi.org/10.1177/21582440221142210 Maas et al. [2011] Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014) Almusharraf [2022] Almusharraf, M.A.M.I.X.D.N.: Automated and human interaction in written discourse: A contrastive parallel corpus-based investigation of metadiscourse features in machine-human translations. Sage Open (2022) https://doi.org/10.1177/21582440221142210 Maas et al. [2011] Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Almusharraf, M.A.M.I.X.D.N.: Automated and human interaction in written discourse: A contrastive parallel corpus-based investigation of metadiscourse features in machine-human translations. Sage Open (2022) https://doi.org/10.1177/21582440221142210 Maas et al. [2011] Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017) Sutskever et al. [2014] Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014) Almusharraf [2022] Almusharraf, M.A.M.I.X.D.N.: Automated and human interaction in written discourse: A contrastive parallel corpus-based investigation of metadiscourse features in machine-human translations. Sage Open (2022) https://doi.org/10.1177/21582440221142210 Maas et al. [2011] Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014) Almusharraf [2022] Almusharraf, M.A.M.I.X.D.N.: Automated and human interaction in written discourse: A contrastive parallel corpus-based investigation of metadiscourse features in machine-human translations. Sage Open (2022) https://doi.org/10.1177/21582440221142210 Maas et al. [2011] Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Almusharraf, M.A.M.I.X.D.N.: Automated and human interaction in written discourse: A contrastive parallel corpus-based investigation of metadiscourse features in machine-human translations. Sage Open (2022) https://doi.org/10.1177/21582440221142210 Maas et al. [2011] Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014) Almusharraf [2022] Almusharraf, M.A.M.I.X.D.N.: Automated and human interaction in written discourse: A contrastive parallel corpus-based investigation of metadiscourse features in machine-human translations. Sage Open (2022) https://doi.org/10.1177/21582440221142210 Maas et al. [2011] Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Almusharraf, M.A.M.I.X.D.N.: Automated and human interaction in written discourse: A contrastive parallel corpus-based investigation of metadiscourse features in machine-human translations. Sage Open (2022) https://doi.org/10.1177/21582440221142210 Maas et al. [2011] Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Almusharraf, M.A.M.I.X.D.N.: Automated and human interaction in written discourse: A contrastive parallel corpus-based investigation of metadiscourse features in machine-human translations. Sage Open (2022) https://doi.org/10.1177/21582440221142210 Maas et al. [2011] Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Maas, A.L., et al.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011) Khan [2023] Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Khan, A.: Improved multi-lingual sentiment analysis and recognition using deep learning. Journal Of Information Science (2023) https://doi.org/10.1177/01655515221137270 Chaudhry et al. [2021] Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Chaudhry, H.N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z.I., Shoaib, U., Janjua, S.H.: Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics 10(17), 2082 (2021) Liang et al. [2022] Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022) Srivastava et al. [2022] Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Srivastava, A., Rastogi, A., Rao, A., Shoeb, A.A.M., Abid, A., Fisch, A., Brown, A.R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al.: Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022) Elmadany et al. [2022] Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Elmadany, A., Nagoudi, E.M.B., Abdul-Mageed, M.: Orca: A challenging benchmark for arabic language understanding. arXiv preprint arXiv:2212.10758 (2022) Abdelali et al. [2023] Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Abdelali, A., Mubarak, H., Chowdhury, S.A., Hasanain, M., Mousi, B., Boughorbel, S., Kheir, Y.E., Izham, D., Dalvi, F., Hawasly, M., et al.: Benchmarking arabic ai with large language models. arXiv preprint arXiv:2305.14982 (2023) Radford et al. [2022] Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356 (2022) Zhang et al. [2023] Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., Chen, N., Li, B., Axelrod, V., Wang, G., et al.: Google usm: Scaling automatic speech recognition beyond 100 languages. arXiv preprint arXiv:2303.01037 (2023) White et al. [2023] White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., Schmidt, D.C.: A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382 (2023) Zhou et al. [2022] Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022) Shin et al. [2020] Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Shin, T., Razeghi, Y., Logan IV, R.L., Wallace, E., Singh, S.: Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020) Lauderdale and Clark [2014] Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science 58(3), 754–771 (2014) Medvedeva et al. [2019] Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Medvedeva, M., Vols, M., Wieling, M.: Using machine learning to predict decisions of the european court of human rights. Artificial Intelligence and Law 27(3), 237–266 (2019) AL-Qurishi et al. [2022] AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- AL-Qurishi, M., AlQaseemi, S., Soussi, R.: AraLegal-BERT: A pretrained language model for Arabic Legal text (2022) Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Sengupta et al. [2023] Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Sengupta, N., Sahu, S.K., Jia, B., Katipomu, S., Li, H., Koto, F., Afzal, O.M., Kamboj, S., Pandit, O., Pal, R., et al.: Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Koubaa [2023] Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Koubaa, A.: Gpt-4 vs. gpt-3.5: A concise showdown. Preprints (2023) https://doi.org/10.20944/preprints202303.0422.v1 Floridi and Chiriatti [2020] Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30, 681–694 (2020) [31] OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- OpenAI: GPT3 Dataset Language Statistics. https://github.com/openai/gpt-3/tree/master/dataset_statistics. Accessed on 09 October 2023 Shibata et al. [1999] Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Shibata, Y., Kida, T., Fukamachi, S., Takeda, M., Shinohara, A., Shinohara, T., Arikawa, S.: Byte pair encoding: A text compression scheme that accelerates pattern matching (1999) Bostrom and Durrett [2020] Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Bostrom, K., Durrett, G.: Byte pair encoding is suboptimal for language model pretraining. arXiv preprint arXiv:2004.03720 (2020) Kudo and Richardson [2018] Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226 (2018) Pu et al. [2023] Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Pu, G., Jain, A., Yin, J., Kaplan, R.: Empirical analysis of the strengths and weaknesses of peft techniques for llms. arXiv preprint arXiv:2304.14999 (2023) Hu et al. [2023] Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Hu, Z., Lan, Y., Wang, L., Xu, W., Lim, E.-P., Lee, R.K.-W., Bing, L., Poria, S.: Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. arXiv preprint arXiv:2304.01933 (2023) [37] AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- AGI-Edgerunners: LLM-Adapters Github repository. https://github.com/AGI-Edgerunners/LLM-Adapters. Accessed on 09 October 2023 Hu et al. [2021] Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021) [39] SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- SJP: Saudi Justice Portal. https://sjp.moj.gov.sa. Accessed on 05 October 2023 [40] PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- PyPI: Selenium Python library. https://pypi.org/project/selenium. Accessed on 05 October 2023 [41] PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- PyPI: Beautiful Soup Python package. https://pypi.org/project/bs4. Accessed on 04 October 2023 [42] PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- PyPI: translators Python package. https://pypi.org/project/translators/. Accessed on 28 September 2023 Papineni et al. [2002] Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002) Chen and Cherry [2014] Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 362–367 (2014) [45] NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- NLTK: Bleu Python package. https://www.nltk.org/api/nltk.translate.bleu_score.html. Accessed on 04 October 2023 Lin [2004] Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004) [47] PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023 PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- PyPI: rouge Python package. https://pypi.org/project/rouge. Accessed on 04 October 2023
- Adel Ammar (24 papers)
- Anis Koubaa (49 papers)
- Bilel Benjdira (17 papers)
- Omar Najar (3 papers)
- Serry Sibaee (9 papers)