Background

Sparse autoencoders recover a diversity of interpretable, monosemantic features, but present an intractable problem of scale to human labelers. We investigate different techniques for generating and scoring text explanations of SAE features.

Key Findings

• Open source models generate and evaluate text explanations of SAE features reasonably well, albeit somewhat worse than closed models like Claude 3.5 Sonnet.
• Explanations found by LLMs are similar to explanations found by humans.
• Automatically interpreting 1.5M features of GPT-2 with the current pipeline would cost $1300 in API calls to Llama 3.1 or $8500 with Claude 3.5 Sonnet. Prior methods cost ~$200k with Claude.
• Code can be found at https://github.com/EleutherAI/sae-auto-interp.
• We built a small dashboard to explore explanations and their scores: https://cadentj.github.io/demo/

Generating Explanations

Sparse autoencoders decompose activations into a sum of sparse feature directions. We leverage language models to generate explanations for activating text examples. Prior work [...]

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