SAELens: Sparse Autoencoders for Mechanistic Interpretability

SAELens is the primary library for training and analyzing Sparse Autoencoders (SAEs) - a technique for decomposing polysemantic neural network activations into sparse, interpretable features. Based on Anthropic's groundbreaking research on monosemanticity.

GitHub: jbloomAus/SAELens (1,100+ stars)

The Problem: Polysemanticity & Superposition

Individual neurons in neural networks are polysemantic - they activate in multiple, semantically distinct contexts. This happens because models use superposition to represent more features than they have neurons, making interpretability difficult.

SAEs solve this by decomposing dense activations into sparse, monosemantic features - typically only a small number of features activate for any given input, and each feature corresponds to an interpretable concept.

When to Use SAELens

Use SAELens when you need to:

• Discover interpretable features in model activations
• Understand what concepts a model has learned
• Study superposition and feature geometry
• Perform feature-based steering or ablation