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Enhancing Multilingual Information Retrieval in Mixed Human Resources Environments: A RAG Model Implementation for Multicultural Enterprise (2401.01511v1)

Published 3 Jan 2024 in cs.IR

Abstract: The advent of LLMs has revolutionized information retrieval, ushering in a new era of expansive knowledge accessibility. While these models excel in providing open-world knowledge, effectively extracting answers in diverse linguistic environments with varying levels of literacy remains a formidable challenge. Retrieval Augmented Generation (RAG) emerges as a promising solution, bridging the gap between information availability and multilingual comprehension. However, deploying RAG models in real-world scenarios demands careful consideration of various factors. This paper addresses the critical challenges associated with implementing RAG models in multicultural environments. We delve into essential considerations, including data feeding strategies, timely updates, mitigation of hallucinations, prevention of erroneous responses, and optimization of delivery speed. Our work involves the integration of a diverse array of tools, meticulously combined to facilitate the seamless adoption of RAG models across languages and literacy levels within a multicultural organizational context. Through strategic tweaks in our approaches, we achieve not only effectiveness but also efficiency, ensuring the accelerated and accurate delivery of information in a manner that is tailored to the unique requirements of multilingual and multicultural settings.

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References (14)
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Zhang et al., CLIP: Connecting Text and Images for Improved Multimodal Understanding, arXiv preprint arXiv:2103.00020, 2021. [10] P. Rajpurkar et al., Squad2.0: Towards Knowledgeable Machines, arXiv preprint arXiv:1806.03822, 2021. [11] Y. Kim et al., Tacotron: Towards End-to-End Speech Synthesis, arXiv preprint arXiv:1703.10135, 2023. [12] M. Lewis et al., BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Understanding, arXiv preprint arXiv:1910.13461, 2019. [13] J. Smith et al., Paragraph-Based Chunking Strategies for Improved Information Retrieval, Journal of Natural Language Processing, 25(2), 123-145, 2017. [14] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, Proceedings of Neural Informations Processing Systems, 2022. [15] Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever, Robust Speech Recognition via Large-Scale Weak Supervision, arXiv preprint arXiv:2212.04356, 2022, eprint 2212.04356, archivePrefix arXiv, primaryClass eess.AS, doi 10.48550/arXiv.2212.04356. K. Lee et al., Retrieval Augmented Generation: Making Pre-trained Language Models Better, NeurIPS, 2021. [6] Y. Liu et al., RoBERTa: A Robustly Optimized BERT Approach, arXiv preprint arXiv:1907.11692, 2019. [7] D. Chen et al., Unsupervised Data Augmentation for Consistency Training, arXiv preprint arXiv:1904.12848, 2020. [8] K. Guu et al., Realm: Retrieval-Augmented Language Model Pre-training, arXiv preprint arXiv:2002.08909, 2020. [9] H. Zhang et al., CLIP: Connecting Text and Images for Improved Multimodal Understanding, arXiv preprint arXiv:2103.00020, 2021. [10] P. Rajpurkar et al., Squad2.0: Towards Knowledgeable Machines, arXiv preprint arXiv:1806.03822, 2021. [11] Y. Kim et al., Tacotron: Towards End-to-End Speech Synthesis, arXiv preprint arXiv:1703.10135, 2023. [12] M. Lewis et al., BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Understanding, arXiv preprint arXiv:1910.13461, 2019. [13] J. Smith et al., Paragraph-Based Chunking Strategies for Improved Information Retrieval, Journal of Natural Language Processing, 25(2), 123-145, 2017. [14] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, Proceedings of Neural Informations Processing Systems, 2022. [15] Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever, Robust Speech Recognition via Large-Scale Weak Supervision, arXiv preprint arXiv:2212.04356, 2022, eprint 2212.04356, archivePrefix arXiv, primaryClass eess.AS, doi 10.48550/arXiv.2212.04356. Y. Liu et al., RoBERTa: A Robustly Optimized BERT Approach, arXiv preprint arXiv:1907.11692, 2019. [7] D. Chen et al., Unsupervised Data Augmentation for Consistency Training, arXiv preprint arXiv:1904.12848, 2020. [8] K. Guu et al., Realm: Retrieval-Augmented Language Model Pre-training, arXiv preprint arXiv:2002.08909, 2020. [9] H. Zhang et al., CLIP: Connecting Text and Images for Improved Multimodal Understanding, arXiv preprint arXiv:2103.00020, 2021. [10] P. Rajpurkar et al., Squad2.0: Towards Knowledgeable Machines, arXiv preprint arXiv:1806.03822, 2021. [11] Y. Kim et al., Tacotron: Towards End-to-End Speech Synthesis, arXiv preprint arXiv:1703.10135, 2023. [12] M. Lewis et al., BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Understanding, arXiv preprint arXiv:1910.13461, 2019. [13] J. Smith et al., Paragraph-Based Chunking Strategies for Improved Information Retrieval, Journal of Natural Language Processing, 25(2), 123-145, 2017. [14] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, Proceedings of Neural Informations Processing Systems, 2022. [15] Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever, Robust Speech Recognition via Large-Scale Weak Supervision, arXiv preprint arXiv:2212.04356, 2022, eprint 2212.04356, archivePrefix arXiv, primaryClass eess.AS, doi 10.48550/arXiv.2212.04356. D. Chen et al., Unsupervised Data Augmentation for Consistency Training, arXiv preprint arXiv:1904.12848, 2020. [8] K. Guu et al., Realm: Retrieval-Augmented Language Model Pre-training, arXiv preprint arXiv:2002.08909, 2020. [9] H. Zhang et al., CLIP: Connecting Text and Images for Improved Multimodal Understanding, arXiv preprint arXiv:2103.00020, 2021. [10] P. Rajpurkar et al., Squad2.0: Towards Knowledgeable Machines, arXiv preprint arXiv:1806.03822, 2021. [11] Y. Kim et al., Tacotron: Towards End-to-End Speech Synthesis, arXiv preprint arXiv:1703.10135, 2023. [12] M. Lewis et al., BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Understanding, arXiv preprint arXiv:1910.13461, 2019. [13] J. Smith et al., Paragraph-Based Chunking Strategies for Improved Information Retrieval, Journal of Natural Language Processing, 25(2), 123-145, 2017. [14] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, Proceedings of Neural Informations Processing Systems, 2022. [15] Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever, Robust Speech Recognition via Large-Scale Weak Supervision, arXiv preprint arXiv:2212.04356, 2022, eprint 2212.04356, archivePrefix arXiv, primaryClass eess.AS, doi 10.48550/arXiv.2212.04356. K. Guu et al., Realm: Retrieval-Augmented Language Model Pre-training, arXiv preprint arXiv:2002.08909, 2020. [9] H. Zhang et al., CLIP: Connecting Text and Images for Improved Multimodal Understanding, arXiv preprint arXiv:2103.00020, 2021. [10] P. Rajpurkar et al., Squad2.0: Towards Knowledgeable Machines, arXiv preprint arXiv:1806.03822, 2021. [11] Y. Kim et al., Tacotron: Towards End-to-End Speech Synthesis, arXiv preprint arXiv:1703.10135, 2023. [12] M. Lewis et al., BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Understanding, arXiv preprint arXiv:1910.13461, 2019. [13] J. Smith et al., Paragraph-Based Chunking Strategies for Improved Information Retrieval, Journal of Natural Language Processing, 25(2), 123-145, 2017. [14] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, Proceedings of Neural Informations Processing Systems, 2022. [15] Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever, Robust Speech Recognition via Large-Scale Weak Supervision, arXiv preprint arXiv:2212.04356, 2022, eprint 2212.04356, archivePrefix arXiv, primaryClass eess.AS, doi 10.48550/arXiv.2212.04356. H. Zhang et al., CLIP: Connecting Text and Images for Improved Multimodal Understanding, arXiv preprint arXiv:2103.00020, 2021. [10] P. Rajpurkar et al., Squad2.0: Towards Knowledgeable Machines, arXiv preprint arXiv:1806.03822, 2021. [11] Y. Kim et al., Tacotron: Towards End-to-End Speech Synthesis, arXiv preprint arXiv:1703.10135, 2023. [12] M. Lewis et al., BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Understanding, arXiv preprint arXiv:1910.13461, 2019. [13] J. Smith et al., Paragraph-Based Chunking Strategies for Improved Information Retrieval, Journal of Natural Language Processing, 25(2), 123-145, 2017. [14] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, Proceedings of Neural Informations Processing Systems, 2022. [15] Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever, Robust Speech Recognition via Large-Scale Weak Supervision, arXiv preprint arXiv:2212.04356, 2022, eprint 2212.04356, archivePrefix arXiv, primaryClass eess.AS, doi 10.48550/arXiv.2212.04356. P. Rajpurkar et al., Squad2.0: Towards Knowledgeable Machines, arXiv preprint arXiv:1806.03822, 2021. [11] Y. Kim et al., Tacotron: Towards End-to-End Speech Synthesis, arXiv preprint arXiv:1703.10135, 2023. [12] M. Lewis et al., BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Understanding, arXiv preprint arXiv:1910.13461, 2019. [13] J. Smith et al., Paragraph-Based Chunking Strategies for Improved Information Retrieval, Journal of Natural Language Processing, 25(2), 123-145, 2017. [14] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, Proceedings of Neural Informations Processing Systems, 2022. [15] Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever, Robust Speech Recognition via Large-Scale Weak Supervision, arXiv preprint arXiv:2212.04356, 2022, eprint 2212.04356, archivePrefix arXiv, primaryClass eess.AS, doi 10.48550/arXiv.2212.04356. Y. Kim et al., Tacotron: Towards End-to-End Speech Synthesis, arXiv preprint arXiv:1703.10135, 2023. [12] M. Lewis et al., BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Understanding, arXiv preprint arXiv:1910.13461, 2019. [13] J. Smith et al., Paragraph-Based Chunking Strategies for Improved Information Retrieval, Journal of Natural Language Processing, 25(2), 123-145, 2017. [14] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, Proceedings of Neural Informations Processing Systems, 2022. [15] Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever, Robust Speech Recognition via Large-Scale Weak Supervision, arXiv preprint arXiv:2212.04356, 2022, eprint 2212.04356, archivePrefix arXiv, primaryClass eess.AS, doi 10.48550/arXiv.2212.04356. M. Lewis et al., BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Understanding, arXiv preprint arXiv:1910.13461, 2019. [13] J. Smith et al., Paragraph-Based Chunking Strategies for Improved Information Retrieval, Journal of Natural Language Processing, 25(2), 123-145, 2017. [14] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, Proceedings of Neural Informations Processing Systems, 2022. [15] Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever, Robust Speech Recognition via Large-Scale Weak Supervision, arXiv preprint arXiv:2212.04356, 2022, eprint 2212.04356, archivePrefix arXiv, primaryClass eess.AS, doi 10.48550/arXiv.2212.04356. J. Smith et al., Paragraph-Based Chunking Strategies for Improved Information Retrieval, Journal of Natural Language Processing, 25(2), 123-145, 2017. [14] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, Proceedings of Neural Informations Processing Systems, 2022. [15] Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever, Robust Speech Recognition via Large-Scale Weak Supervision, arXiv preprint arXiv:2212.04356, 2022, eprint 2212.04356, archivePrefix arXiv, primaryClass eess.AS, doi 10.48550/arXiv.2212.04356. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, Proceedings of Neural Informations Processing Systems, 2022. [15] Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever, Robust Speech Recognition via Large-Scale Weak Supervision, arXiv preprint arXiv:2212.04356, 2022, eprint 2212.04356, archivePrefix arXiv, primaryClass eess.AS, doi 10.48550/arXiv.2212.04356. Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever, Robust Speech Recognition via Large-Scale Weak Supervision, arXiv preprint arXiv:2212.04356, 2022, eprint 2212.04356, archivePrefix arXiv, primaryClass eess.AS, doi 10.48550/arXiv.2212.04356.
  2. J. Smith et al., Enhancing Multilingual Communication in Global Enterprises, International Journal of Business Communication, 46(3), 283-304, 2018. [4] H. Wang et al., Frameworks for Multilingual Communication in Enterprises, Journal of Enterprise Information Management, 35(1), 148-167, 2022. [5] K. Lee et al., Retrieval Augmented Generation: Making Pre-trained Language Models Better, NeurIPS, 2021. [6] Y. Liu et al., RoBERTa: A Robustly Optimized BERT Approach, arXiv preprint arXiv:1907.11692, 2019. [7] D. Chen et al., Unsupervised Data Augmentation for Consistency Training, arXiv preprint arXiv:1904.12848, 2020. [8] K. Guu et al., Realm: Retrieval-Augmented Language Model Pre-training, arXiv preprint arXiv:2002.08909, 2020. [9] H. Zhang et al., CLIP: Connecting Text and Images for Improved Multimodal Understanding, arXiv preprint arXiv:2103.00020, 2021. [10] P. Rajpurkar et al., Squad2.0: Towards Knowledgeable Machines, arXiv preprint arXiv:1806.03822, 2021. [11] Y. Kim et al., Tacotron: Towards End-to-End Speech Synthesis, arXiv preprint arXiv:1703.10135, 2023. [12] M. Lewis et al., BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Understanding, arXiv preprint arXiv:1910.13461, 2019. [13] J. Smith et al., Paragraph-Based Chunking Strategies for Improved Information Retrieval, Journal of Natural Language Processing, 25(2), 123-145, 2017. [14] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, Proceedings of Neural Informations Processing Systems, 2022. [15] Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever, Robust Speech Recognition via Large-Scale Weak Supervision, arXiv preprint arXiv:2212.04356, 2022, eprint 2212.04356, archivePrefix arXiv, primaryClass eess.AS, doi 10.48550/arXiv.2212.04356. H. Wang et al., Frameworks for Multilingual Communication in Enterprises, Journal of Enterprise Information Management, 35(1), 148-167, 2022. [5] K. Lee et al., Retrieval Augmented Generation: Making Pre-trained Language Models Better, NeurIPS, 2021. [6] Y. Liu et al., RoBERTa: A Robustly Optimized BERT Approach, arXiv preprint arXiv:1907.11692, 2019. [7] D. Chen et al., Unsupervised Data Augmentation for Consistency Training, arXiv preprint arXiv:1904.12848, 2020. [8] K. Guu et al., Realm: Retrieval-Augmented Language Model Pre-training, arXiv preprint arXiv:2002.08909, 2020. [9] H. Zhang et al., CLIP: Connecting Text and Images for Improved Multimodal Understanding, arXiv preprint arXiv:2103.00020, 2021. [10] P. Rajpurkar et al., Squad2.0: Towards Knowledgeable Machines, arXiv preprint arXiv:1806.03822, 2021. [11] Y. Kim et al., Tacotron: Towards End-to-End Speech Synthesis, arXiv preprint arXiv:1703.10135, 2023. [12] M. Lewis et al., BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Understanding, arXiv preprint arXiv:1910.13461, 2019. [13] J. Smith et al., Paragraph-Based Chunking Strategies for Improved Information Retrieval, Journal of Natural Language Processing, 25(2), 123-145, 2017. [14] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, Proceedings of Neural Informations Processing Systems, 2022. [15] Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever, Robust Speech Recognition via Large-Scale Weak Supervision, arXiv preprint arXiv:2212.04356, 2022, eprint 2212.04356, archivePrefix arXiv, primaryClass eess.AS, doi 10.48550/arXiv.2212.04356. K. Lee et al., Retrieval Augmented Generation: Making Pre-trained Language Models Better, NeurIPS, 2021. [6] Y. Liu et al., RoBERTa: A Robustly Optimized BERT Approach, arXiv preprint arXiv:1907.11692, 2019. [7] D. Chen et al., Unsupervised Data Augmentation for Consistency Training, arXiv preprint arXiv:1904.12848, 2020. [8] K. Guu et al., Realm: Retrieval-Augmented Language Model Pre-training, arXiv preprint arXiv:2002.08909, 2020. [9] H. Zhang et al., CLIP: Connecting Text and Images for Improved Multimodal Understanding, arXiv preprint arXiv:2103.00020, 2021. [10] P. Rajpurkar et al., Squad2.0: Towards Knowledgeable Machines, arXiv preprint arXiv:1806.03822, 2021. [11] Y. Kim et al., Tacotron: Towards End-to-End Speech Synthesis, arXiv preprint arXiv:1703.10135, 2023. [12] M. Lewis et al., BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Understanding, arXiv preprint arXiv:1910.13461, 2019. [13] J. Smith et al., Paragraph-Based Chunking Strategies for Improved Information Retrieval, Journal of Natural Language Processing, 25(2), 123-145, 2017. [14] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, Proceedings of Neural Informations Processing Systems, 2022. [15] Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever, Robust Speech Recognition via Large-Scale Weak Supervision, arXiv preprint arXiv:2212.04356, 2022, eprint 2212.04356, archivePrefix arXiv, primaryClass eess.AS, doi 10.48550/arXiv.2212.04356. Y. Liu et al., RoBERTa: A Robustly Optimized BERT Approach, arXiv preprint arXiv:1907.11692, 2019. [7] D. Chen et al., Unsupervised Data Augmentation for Consistency Training, arXiv preprint arXiv:1904.12848, 2020. [8] K. Guu et al., Realm: Retrieval-Augmented Language Model Pre-training, arXiv preprint arXiv:2002.08909, 2020. [9] H. Zhang et al., CLIP: Connecting Text and Images for Improved Multimodal Understanding, arXiv preprint arXiv:2103.00020, 2021. [10] P. Rajpurkar et al., Squad2.0: Towards Knowledgeable Machines, arXiv preprint arXiv:1806.03822, 2021. [11] Y. Kim et al., Tacotron: Towards End-to-End Speech Synthesis, arXiv preprint arXiv:1703.10135, 2023. [12] M. Lewis et al., BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Understanding, arXiv preprint arXiv:1910.13461, 2019. [13] J. Smith et al., Paragraph-Based Chunking Strategies for Improved Information Retrieval, Journal of Natural Language Processing, 25(2), 123-145, 2017. [14] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, Proceedings of Neural Informations Processing Systems, 2022. 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  12. J. Smith et al., Paragraph-Based Chunking Strategies for Improved Information Retrieval, Journal of Natural Language Processing, 25(2), 123-145, 2017. [14] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, Proceedings of Neural Informations Processing Systems, 2022. [15] Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever, Robust Speech Recognition via Large-Scale Weak Supervision, arXiv preprint arXiv:2212.04356, 2022, eprint 2212.04356, archivePrefix arXiv, primaryClass eess.AS, doi 10.48550/arXiv.2212.04356. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, Proceedings of Neural Informations Processing Systems, 2022. [15] Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever, Robust Speech Recognition via Large-Scale Weak Supervision, arXiv preprint arXiv:2212.04356, 2022, eprint 2212.04356, archivePrefix arXiv, primaryClass eess.AS, doi 10.48550/arXiv.2212.04356. Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever, Robust Speech Recognition via Large-Scale Weak Supervision, arXiv preprint arXiv:2212.04356, 2022, eprint 2212.04356, archivePrefix arXiv, primaryClass eess.AS, doi 10.48550/arXiv.2212.04356.
  13. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, Proceedings of Neural Informations Processing Systems, 2022. [15] Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever, Robust Speech Recognition via Large-Scale Weak Supervision, arXiv preprint arXiv:2212.04356, 2022, eprint 2212.04356, archivePrefix arXiv, primaryClass eess.AS, doi 10.48550/arXiv.2212.04356. Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever, Robust Speech Recognition via Large-Scale Weak Supervision, arXiv preprint arXiv:2212.04356, 2022, eprint 2212.04356, archivePrefix arXiv, primaryClass eess.AS, doi 10.48550/arXiv.2212.04356.
  14. Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever, Robust Speech Recognition via Large-Scale Weak Supervision, arXiv preprint arXiv:2212.04356, 2022, eprint 2212.04356, archivePrefix arXiv, primaryClass eess.AS, doi 10.48550/arXiv.2212.04356.
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