  Localizing behaviors of neural networks to a subset of the network's
components or a subset of interactions between components is a natural first
step towards analyzing network mechanisms and possible failure modes. Existing
work is often qualitative and ad-hoc, and there is no consensus on the
appropriate way to evaluate localization claims. We introduce path patching, a
technique for expressing and quantitatively testing a natural class of
hypotheses expressing that behaviors are localized to a set of paths. We refine
an explanation of induction heads, characterize a behavior of GPT-2, and open
source a framework for efficiently running similar experiments.
