An Intuitive Explanation of Sparse Autoencoders for Mechanistic Interpretability of LLMs — LessWrong

Sparse Autoencoders (SAEs) have recently become popular for interpretability of machine learning models (although SAEs have been around since 1997). Machine learning models and LLMs are becoming more powerful and useful, but they are still black boxes, and we don't understand how they do the things that they are capable of. It seems like it would be useful if we could understand how they work. Using SAEs, we can begin to break down a model's computation into understandable components. There are several existing explanations of SAEs, and I wanted to create a brief writeup from a different angle with an intuitive explanation of how they work. The most natural component of a neural network is individual neurons. Unfortunately, individual neurons do not conveniently correspond to single concepts. An example neuron in a language model corresponded to academic citations, English dialogue, HTTP requests, and Korean text. This is a concept called superposition, where concepts in a neural netwo

x An Intuitive Explanation of Sparse Autoencoders for Mechanistic Interpretability of LLMs — LessWrong Sparse Autoencoders (SAEs) AI Frontpage 30 An Intuitive Explanation of Sparse Autoencoders for Mechanistic Interpretability of LLMs by Adam Karvonen 25th Jun 2024 Linkpost for adamkarvonen.github.io 10 min read 0 30 Sparse Autoencoders (SAEs) have recently become popular for interpretability of machine learning models (although SAEs have been around since 1997 ). Machine learning models and LLMs are becoming more powerful and useful, but they are still black boxes, and we don't understand how

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