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“Open Source Automated Interpretability for Sparse Autoencoder Features” by kh4dien, SrGonao, jacob_drori, Nora Belrose


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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 [...]

The original text contained 3 images which were described by AI.

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First published:

July 30th, 2024

Source:

https://www.lesswrong.com/posts/AhG3RJ6F5KvmKmAkd/open-source-automated-interpretability-for-sparse

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Narrated by TYPE III AUDIO.

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