Attribution Patching Outperforms Automated Circuit Discovery

NeurIPS 2023 Workshop ATTRIB Submission47 Authors

Published: 27 Oct 2023, Last Modified: 08 Dec 2023ATTRIB PosterEveryoneRevisionsBibTeX
Keywords: Interpretability, Automated Interpretability, Mechanistic Interpretability, Large Language Models, Natural Language Processing, Science of Deep Learning
TL;DR: We find that a simple attribution patching method outperforms existing expensive ways to find circuits.
Abstract: Automated interpretability research has recently attracted attention as a potential research direction that could scale explanations of neural network behavior to large models. Existing automated circuit discovery work applies activation patching to identify subnetworks responsible for solving specific tasks (circuits). In this work, we show that a simple method based on attribution patching outperforms all existing methods while requiring just two forward passes and a backward pass. We apply a linear approximation to activation patching to estimate the importance of each edge in the computational subgraph. Using this approximation, we prune the least important edges of the network. We survey the performance and limitations of this method, finding that averaged over all tasks our method has greater AUC from circuit recovery than other methods.
Submission Number: 47