The study demonstrates how text generation by LLMs aligns with Bayesian learning principles and explores the implications for in-context learning. It explains why in-context learning emerges in larger models where prompts are considered as samples to be updated. The findings suggest that the behavior of LLMs is consistent with Bayesian Learning, providing new insights into their functioning and potential applications.
Key takeaways:
- The paper introduces a Bayesian learning model to understand the behavior of Large Language Models (LLMs).
- The study involves constructing an ideal generative text model represented by a multinomial transition probability matrix with a prior.
- The research presents the Dirichlet approximation theorem to approximate any prior and discusses the continuity of the mapping between embeddings and multinomial distributions.
- The findings suggest that the behavior of LLMs aligns with Bayesian Learning principles, particularly explaining why in-context learning emerges in larger models.