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“SAEBench: A Comprehensive Benchmark for Sparse Autoencoders” by Can, Adam Karvonen, Johnny Lin, Curt Tigges, Joseph Bloom, chanind, Yeu-Tong Lau, Eoin Farrell, Arthur Conmy, CallumMcDougall, Kola Ayonrinde, Matthew Wearden, Sam Marks, Neel Nanda

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Adam Karvonen*, Can Rager*, Johnny Lin*, Curt Tigges*, Joseph Bloom*, David Chanin, Yeu-Tong Lau, Eoin Farrell, Arthur Conmy, Callum McDougall, Kola Ayonrinde, Matthew Wearden, Samuel Marks, Neel Nanda *equal contribution

TL;DR

  • We are releasing SAE Bench, a suite of 8 diverse sparse autoencoder (SAE) evaluations including unsupervised metrics and downstream tasks. Use our codebase to evaluate your own SAEs!
  • You can compare 200+ SAEs of varying sparsity, dictionary size, architecture, and training time on Neuronpedia.
  • Think we're missing an eval? We'd love for you to contribute it to our codebase! Email us.

🔍 Explore the Benchmark & Rankings

📊 Evaluate your SAEs with SAEBench

✉️ Contact Us

Introduction

Sparse Autoencoders (SAEs) have become one of the most popular tools for AI interpretability. A lot of recent interpretability work has been focused on studying SAEs, in particular on improving SAEs, e.g. the Gated [...]





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Outline:

(00:31) TL;DR

(01:18) Introduction

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First published:

December 11th, 2024

Source:

https://www.lesswrong.com/posts/jGG24BzLdYvi9dugm/saebench-a-comprehensive-benchmark-for-sparse-autoencoders

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Narrated by TYPE III AUDIO.

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