Understanding LLMs Like Physicists: Observation, Hypothesis, Experimentation, and Prediction

A Google TechTalk, presented by Tianyu Guo, 2025-02-20 Google Algorithms Seminar: ABSTRACT: Recently, methodologies from physics have inspired new research paradigms for scientific understandings of LLMs. In physics, knowledge often emerges through four stages: observing nature, forming hypotheses, conducting controlled experiments, and making real-world predictions. Here, I present two independent mechanisms discovered in LLMs following this methodology. Dormant Heads: LLMs deactivate certain attention heads when they are irrelevant to the current task. A given head may serve a specific function, and when faced with an unrelated prompt, it becomes dormant, concentrating all attention on the first token. Random Guessing in Two-Hop Reasoning: Pretrained LLMs resort to random guesses when distractions are present in two-hop reasoning. A well-designed supervised fine-tuning dataset can solve this issue. I will discuss how these mechanisms emerge through observations, how hypotheses are formed, how we design and analyze controlled experiments, and how these mechanisms are validated in LLMs. ABOUT THE SPEAKER: Tianyu Guo is a third-year PhD student in the UC Berkeley Statistics Department, advised by Song Mei and Michael I. Jordan. His research focuses on the Interpretability of Large Language models and Causal Inference.