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TopicsSparse Autoencoders Machine Learning Mechanistic Interpretability

Sparse autoencoder latents can reveal spurious differences between labeled datasets.

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One use case where we did find SAEs useful was in dataset debugging.

Negative Results for SAEs On Downstream Tasks and Deprioritising SAE Research (GDM Mech Interp Team Progress Update #2)blog postextraction 0.75

lewis smith et al. · original

SAE latents revealed spurious differences (e.g. question marks only in Alpaca) between harmful and harmless datasets; authors note simpler methods such as linear-probe max-activating examples could achieve similar results.

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