Negative Results for Sparse Autoencoders On Downstream Tasks and Deprioritising SAE Research…

Summary

Google DeepMind's mechanistic interpretability team tested whether sparse autoencoders (SAEs) help with out-of-distribution detection of harmful intent in user prompts. SAEs underperformed linear probes, which the team found cheap and strong. As a result, the team is deprioritising fundamental SAE research while keeping SAEs as one tool.

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