sparse-autoencoder-training

To discover interpretable features in neural network activations, you train Sparse Autoencoders to decompose complex internal representations into monosemantic concepts. This process isolates individual features that activate for specific linguistic phenomena, grammatical structures, or factual knowledge within language models.

Mechanistic interpretability studies how models represent information internally. Sparse autoencoders help by analyzing superposition and decomposing dense neural network activations into simpler, monosemantic representations, making the internal workings of language models understandable and isolating specific learned concepts.

Yes, you need PyTorch, TransformerLens, and SAELens to train and analyze sparse autoencoders. These libraries provide the necessary environment for loading language models, accessing internal activations, and running the feature discovery and feature-based steering operations.

You analyze features in a language model like GPT-2 small by loading a pre-trained SAE using SAELens and evaluating its activations on target text. This reveals which interpretable features fire for specific inputs, such as analyzing sentiment or factual knowledge representations.

Yes, sparse autoencoders facilitate feature-based steering in language models. By decomposing activations into interpretable features, you can identify and manipulate specific monosemantic representations to directly influence or guide the model's output behavior during generation.