Sparse Autoencoders Reveal Universal Feature Spaces Across Large Language Models

• URL: http://arxiv.org/abs/2410.06981v2
• Date: Fri, 31 Jan 2025 15:27:10 GMT
• Title: Sparse Autoencoders Reveal Universal Feature Spaces Across Large Language Models
• Authors: Michael Lan, Philip Torr, Austin Meek, Ashkan Khakzar, David Krueger, Fazl Barez,
• Abstract summary: Demonstrating feature universality allows discoveries about latent representations to generalize across several models. We employ a method known as dictionary learning to transform LLM activations into more interpretable spaces spanned by neurons corresponding to individual features. Our experiments reveal significant similarities in SAE feature spaces across various LLMs, providing new evidence for feature universality.
• Score: 14.594698598522797
• License:
• Abstract: We investigate feature universality in large language models (LLMs), a research field that aims to understand how different models similarly represent concepts in the latent spaces of their intermediate layers. Demonstrating feature universality allows discoveries about latent representations to generalize across several models. However, comparing features across LLMs is challenging due to polysemanticity, in which individual neurons often correspond to multiple features rather than distinct ones, making it difficult to disentangle and match features across different models. To address this issue, we employ a method known as dictionary learning by using sparse autoencoders (SAEs) to transform LLM activations into more interpretable spaces spanned by neurons corresponding to individual features. After matching feature neurons across models via activation correlation, we apply representational space similarity metrics on SAE feature spaces across different LLMs. Our experiments reveal significant similarities in SAE feature spaces across various LLMs, providing new evidence for feature universality.

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