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This chapter is adapted from a talk delivered by Zeyuan Allen-Zhu, ScD, at the ICML 2024 Tutorial: Physics of Language Models. The talk was widely acclaimed for its depth and systematic approach to understanding the underlying mechanisms of language models. To ensure accessibility and enhance understanding, the content has been transcribed and supplemented with technical commentary. Certain parts of the talk have been edited for clarity and context while maintaining the integrity and intent of the original presentation. By combining rigorous experimentation with synthetic data and probing techniques, the speaker provides insights that challenge traditional approaches to AI development. This chapter offers both theoretical insights and actionable strategies, making it a significant contribution to the ongoing conversation about advancing AI capabilities and understanding.

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Physics of Large Language Models

  • 章节

  • 发表日期：

    2025年

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  • Vikram Dhillon,
  • David Metcalf,
  • Max Hooper

摘要

This chapter is adapted from a talk delivered by Zeyuan Allen-Zhu, ScD, at the ICML 2024 Tutorial: Physics of Language Models. The talk was widely acclaimed for its depth and systematic approach to understanding the underlying mechanisms of language models. To ensure accessibility and enhance understanding, the content has been transcribed and supplemented with technical commentary. Certain parts of the talk have been edited for clarity and context while maintaining the integrity and intent of the original presentation. By combining rigorous experimentation with synthetic data and probing techniques, the speaker provides insights that challenge traditional approaches to AI development. This chapter offers both theoretical insights and actionable strategies, making it a significant contribution to the ongoing conversation about advancing AI capabilities and understanding.