Towards Monosemanticity: Decomposing Language Models With Dictionary Learning

Using a sparse autoencoder, we extract a large number of interpretable features from a one-layer transformer.Mechanistic interpretability seeks to understand neural networks by breaking them into components that are more easily understood than the whole. By understanding the function of each component, and how they interact, we hope to be able to reason about the behavior of the entire network. The first step in that program is to identify the correct components to analyze.Unfortunately, the ...