Papers › Localizing Model Behavior with Path Patching

Localizing Model Behavior with Path Patching

12 Apr 2023arXiv:2304.05969archive 2025-07-28

Nicholas Goldowsky-Dill, Chris MacLeod, Lucas Sato, Aryaman Arora

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.

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