sparse-autoencoder-training

To train a sparse autoencoder for feature discovery, you decompose neural network activations into interpretable features using libraries like SAELens and PyTorch. This process helps identify what concepts a model has learned by analyzing internal representations.

A sparse autoencoder is used in machine learning interpretability to decompose neural network activations into interpretable features. It enables feature discovery, superposition analysis, and safety assessment by identifying learned concepts within models.

Yes, you need PyTorch, TransformerLens, and SAELens to train sparse autoencoders on GPT-2 activations. These machine learning libraries provide the necessary environment for extracting model activations and executing the training process.

Yes, you can use sparse autoencoders for feature-based steering and safety analysis. By decomposing activations into interpretable features, you can perform ablations and analyze safety-relevant features like deception, bias, or harmful content.

The best way to analyze superposition and feature geometry is by training sparse autoencoders to decompose neural network activations. This approach reveals the underlying interpretable features and concepts the language model has learned.