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Over 70% of sparse autoencoder features exhibit fundamentally mismatched input and output semantics, breaking the common assumption that what activates a feature directly mirrors its downstream causal effect.
Directly verbalizing sparse autoencoder features from LLM representations transforms how we interpret model behavior, making explanations more efficient and insightful.
