Interpretability without actionability: mechanistic methods cannot correct language model errors despite near-perfect internal representations

Interpretability without actionability: mechanistic methods cannot correct language model errors despite near-perfect internal representations explores This research investigates the limitations of current mechanistic interpretability methods in correcting language model errors, finding they cannot reliably bridge the gap between internal knowledge and actionable output correction.. Commercial viability score: 4/10 in LLM Interpretability.