sparse-autoencoder-training

To decompose neural network activations into interpretable features, you train Sparse Autoencoders (SAEs) to resolve polysemanticity and superposition in language models. This Skill guides you through the training and analysis process using SAELens and TransformerLens.

The best way to train a sparse autoencoder for language model interpretability is using the SAELens library alongside TransformerLens and PyTorch. This Skill provides scripts and tutorials to train SAEs that identify monosemantic representations from model activations.

Yes, you need PyTorch, TransformerLens, and SAELens to analyze superposition with sparse autoencoders. These dependencies are required to extract neural network activations and train SAEs for feature discovery and interpretability analysis.

Sparse autoencoder training helps understand neural network behavior by decomposing complex activations into monosemantic, interpretable features. This addresses the challenges of polysemanticity and superposition, allowing you to analyze specific feature representations within language models.

Yes, you can use SAELens to discover monosemantic representations in language model activations. This Skill leverages SAELens and TransformerLens to train sparse autoencoders that identify interpretable features and analyze superposition within neural networks.

Limitations of using sparse autoencoders for feature discovery include the computational requirements of training SAEs with PyTorch and the complexity of resolving polysemanticity in large neural networks. Effective analysis requires proper configuration of SAELens and TransformerLens dependencies.