Attribution Patching Outperforms Automated Circuit Discovery

Understand

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.