Features that Make a Difference: Leveraging Gradients for Improved Dictionary Learning

Authors: Jeffrey Olmo, Jared Wilson, Max Forsey, Bryce Hepner, Thomas Vincent Howe, David Wingate
Primary area: interpretability and explainable AI
Keywords: deep learning, sparse autoencoders, dictionary learning, mechanistic interpretability, large language models, activation function, feature extraction

Abstract:
Sparse Autoencoders (SAEs) are a promising approach for extracting neural network representations by learning a sparse and overcomplete decomposition of the network's internal activations. However, SAEs are traditionally trained considering only activation values and not the effect those activations have on downstream computations. This limits the information available to learn features, and biases the autoencoder towards neglecting small but functionally significant aspects of activations. To address this, we introduce Gradient SAEs (g-SAEs), which modify the $k$-sparse autoencoder architecture by augmenting the topK activation function to rely on the gradients of the input activation when selecting the $k$ elements. For a given sparsity level, g-SAEs produce reconstructions that are more faithful to original network performance when propagated through the network, while facilitating more complete use of the SAE's capacity. On average, g-SAE features are more functionally influential than features recovered with traditional SAEs. By considering the downstream effects of activations, our approach leverages the dual nature of neural network features as both $\textit{representations}$, retrospectively, and $\textit{actions}$, prospectively. While previous methods have approached the problem of feature discovery primarily focused on the former aspect, we believe that g-SAEs represent a step towards accounting for the latter as well.

Decision: reject

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