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Localizing Model Behavior with Path Patching

2023-04-12Code Available1· sign in to hype

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

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Abstract

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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