Total: 1
Interpretability findings on large language models often fail to generalise, and causal claims can outrun the evidence. A recurring source, we suggest, is a borrowed vocabulary: interpretability inherited the words of causality — "mechanisms", "circuits", "features" — without always inheriting the bookkeeping that keeps causal claims precise. This tutorial teaches that bookkeeping for practitioners. Participants leave able to take any interpretability result and answer three questions: what claim does this method license, what would strengthen the claim, and when will it generalise?