Attention Is Bayesian Inference (medium.com)

Vishal Misra and his collaborators have published a trilogy of papers that reveal a groundbreaking insight into transformer models: they inherently perform Bayesian inference through their attention mechanisms. Initially attempting to build a natural language interface for cricket statistics, Misra discovered that while large language models (LLMs) like GPT-3 struggled to provide accurate statistical answers, they could effectively translate complex queries into structured data fetches. This realization led to the development of a method called Retrieval-Augmented Generation (RAG), which demonstrated a significant uptick in accuracy and usage of their system. Their research showed that the architecture of transformers naturally sculpts them into inference engines, capable of maintaining and updating beliefs in a manner akin to Bayesian updating. By creating "Bayesian Wind Tunnels" for testing, the team was able to confirm that transformers consistently build and navigate hypothesis spaces, analogous to playing a game of 20 Questions. This understanding of transformer geometry not only explains their performance on inference tasks but also has implications for enhancing model training methods, particularly using techniques inspired by Expectation-Maximization to facilitate more efficient probabilistic reasoning. The findings suggest that the principles of Bayesian inference are not just additional features of transformers but represent the core computational mechanisms at play in every forward pass.