Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
38 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
41 tokens/sec
o3 Pro
7 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

On the Multilingual Ability of Decoder-based Pre-trained Language Models: Finding and Controlling Language-Specific Neurons (2404.02431v1)

Published 3 Apr 2024 in cs.CL
On the Multilingual Ability of Decoder-based Pre-trained Language Models: Finding and Controlling Language-Specific Neurons

Abstract: Current decoder-based pre-trained LLMs (PLMs) successfully demonstrate multilingual capabilities. However, it is unclear how these models handle multilingualism. We analyze the neuron-level internal behavior of multilingual decoder-based PLMs, Specifically examining the existence of neurons that fire ``uniquely for each language'' within decoder-only multilingual PLMs. We analyze six languages: English, German, French, Spanish, Chinese, and Japanese, and show that language-specific neurons are unique, with a slight overlap (< 5%) between languages. These neurons are mainly distributed in the models' first and last few layers. This trend remains consistent across languages and models. Additionally, we tamper with less than 1% of the total neurons in each model during inference and demonstrate that tampering with a few language-specific neurons drastically changes the probability of target language occurrence in text generation.

PDF HTML Abstract

Deciphering the Multilingual Capabilities of Decoder-Based Pre-trained LLMs Through Language-Specific Neurons

Introduction

In the exploration of multilingual abilities within pre-trained LLMs (PLMs), particularly those that are decoder-based, a novel paper probes the internal mechanics of these models in handling multiple languages. This research dissects the presence and functionality of language-specific neurons within decoder-only PLMs, focusing on the distribution and impact of these neurons across a range of languages and model architectures. Unlike prior studies that mainly centered on language-universal neurons and encoder-based PLMs, this work explores the critical analysis of how specific neurons cater to individual languages, therefore elaborating on the intricate neuronal activity that facilitates multilingual text generation.

Methodology

The paper adopts a refined approach originally proposed by \citet{cuadros2022self} for identifying neurons that respond significantly to inputs of a particular language. Identifying language-specific neurons involved two main phases: preparation of language-specific text corpuses for six languages (English, German, French, Spanish, Chinese, and Japanese) and observation of neurons' activation patterns in response to these texts. Neurons that showed a high activation for texts in a target language (positive sentences) and low activation for other languages (negative sentences) were flagged as language-specific. This neuron identification process was a critical step toward understanding how decoder-based PLMs process multilingual input.

Results

Analyzing various decoder-based PLMs, including XGLM, BLOOM, and Llama2 models, for the specified languages yielded consistent patterns. Language-specific neurons predominantly reside in the models' first and last few layers, with a minimal overlap of less than 5% between languages. The research highlights an intriguing uniformity across multiple languages and model variants in the distribution of these critical neurons.

Furthermore, tampering with these neurons during inference demonstrated a significant shift in the generated text's language, validating the substantial role of language-specific neurons in directing the model's output language. These interventions could successfully manipulate the language of the generated text, showcasing a tangible application of the findings.

Implications and Future Directions

The implications of this research are multifold, touching on theoretical and practical aspects of working with multilingual PLMs. Theoretically, it provides a novel lens to view the internal workings of decoder-based PLMs, enriching our understanding of multilingual text generation. Practically, identifying and manipulating language-specific neurons paves the way for enhanced control over LLMs, particularly in tasks requiring precise language output.

The avenue for future research is vast, with possibilities ranging from the development of language-specific model compression techniques to innovative fine-tuning approaches that could enhance models' performance on languages not included in their training data. Moreover, extending this analysis to a broader range of languages and model architectures, including encoder-decoder frameworks, could further deepen our understanding of PLMs' multilingual capabilities.

Conclusion

This paper marks a significant step in unraveling the complexities of multilingual text generation within decoder-based PLMs, spotlighting the underexplored terrain of language-specific neurons. By methodically identifying and demonstrating the functionality of these neurons across different languages and model variants, the research offers valuable insights and opens new pathways for leveraging the multilingual capacities of PLMs more effectively.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (33)
  1. Omer Antverg and Yonatan Belinkov. 2022. On the pitfalls of analyzing individual neurons in language models. In International Conference on Learning Representations.
  2. Identifying and controlling important neurons in neural machine translation. In International Conference on Learning Representations.
  3. Natural language processing with Python: analyzing text with the natural language toolkit. " O’Reilly Media, Inc.".
  4. Findings of the 2016 conference on machine translation. In Proceedings of the First Conference on Machine Translation, pages 131–198, Berlin, Germany. Association for Computational Linguistics.
  5. Findings of the 2018 conference on machine translation (wmt18). In Proceedings of the Third Conference on Machine Translation, Volume 2: Shared Task Papers, pages 272–307, Belgium, Brussels. Association for Computational Linguistics.
  6. Findings of the 2014 workshop on statistical machine translation. In Proceedings of the Ninth Workshop on Statistical Machine Translation, pages 12–58, Baltimore, Maryland, USA. Association for Computational Linguistics.
  7. Searching for needles in a haystack: On the role of incidental bilingualism in PaLM’s translation capability. arXiv preprint arXiv:2305.10266.
  8. Overview of the iwslt 2017 evaluation campaign. In Proceedings of the 14th International Workshop on Spoken Language Translation, pages 2–14.
  9. Journey to the center of the knowledge neurons: Discoveries of language-independent knowledge neurons and degenerate knowledge neurons. arXiv preprint arXiv:2308.13198.
  10. No language left behind: Scaling human-centered machine translation. arXiv preprint arXiv:2207.04672.
  11. Self-conditioning pre-trained language models. In International Conference on Machine Learning, pages 4455–4473. PMLR.
  12. Knowledge neurons in pretrained transformers. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8493–8502, Dublin, Ireland. Association for Computational Linguistics.
  13. Fasttext.zip: Compressing text classification models.
  14. Bag of tricks for efficient text classification. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 427–431, Valencia, Spain. Association for Computational Linguistics.
  15. Few-shot learning with multilingual language models. arXiv preprint arXiv:2112.10668.
  16. Jesse Mu and Jacob Andreas. 2020. Compositional explanations of neurons. Advances in Neural Information Processing Systems, 33:17153–17163.
  17. Causal analysis of syntactic agreement neurons in multilingual language models. arXiv preprint arXiv:2210.14328.
  18. First align, then predict: Understanding the cross-lingual ability of multilingual BERT. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2214–2231, Online. Association for Computational Linguistics.
  19. OpenAI. 2023. GPT-4 technical report. arXiv, pages 2303–08774.
  20. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311–318.
  21. How multilingual is multilingual BERT? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4996–5001, Florence, Italy. Association for Computational Linguistics.
  22. Neuron-level interpretation of deep nlp models: A survey. Transactions of the Association for Computational Linguistics, 10:1285–1303.
  23. Bloom: A 176b-parameter open-access multilingual language model. arXiv preprint arXiv:2211.05100.
  24. A latent-variable model for intrinsic probing. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 13591–13599.
  25. Same neurons, different languages: Probing morphosyntax in multilingual pre-trained models. arXiv preprint arXiv:2205.02023.
  26. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288.
  27. Andrea Gregor de Varda and Marco Marelli. 2023. Data-driven cross-lingual syntax: An agreement study with massively multilingual models. Computational Linguistics, 49(2):261–299.
  28. Finding skill neurons in pre-trained transformer-based language models. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11132–11152, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
  29. Do Llamas work in English? On the latent language of multilingual transformers. arXiv preprint arXiv:2402.10588.
  30. CCNet: Extracting high quality monolingual datasets from web crawl data. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 4003–4012, Marseille, France. European Language Resources Association.
  31. Huggingface’s transformers: State-of-the-art natural language processing. arXiv preprint arXiv:1910.03771.
  32. mT5: A massively multilingual pre-trained text-to-text transformer. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 483–498, Online. Association for Computational Linguistics.
  33. PAWS-X: A cross-lingual adversarial dataset for paraphrase identification. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3687–3692, Hong Kong, China. Association for Computational Linguistics.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (5)
  1. Takeshi Kojima (9 papers)
  2. Itsuki Okimura (1 paper)
  3. Yusuke Iwasawa (43 papers)
  4. Hitomi Yanaka (29 papers)
  5. Yutaka Matsuo (128 papers)
Citations (16)
View on Semantic Scholar
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets