  Activation Patching is a method of directly computing causal attributions of
behavior to model components. However, applying it exhaustively requires a
sweep with cost scaling linearly in the number of model components, which can
be prohibitively expensive for SoTA Large Language Models (LLMs). We
investigate Attribution Patching (AtP), a fast gradient-based approximation to
Activation Patching and find two classes of failure modes of AtP which lead to
significant false negatives. We propose a variant of AtP called AtP*, with two
changes to address these failure modes while retaining scalability. We present
the first systematic study of AtP and alternative methods for faster activation
patching and show that AtP significantly outperforms all other investigated
methods, with AtP* providing further significant improvement. Finally, we
provide a method to bound the probability of remaining false negatives of AtP*
estimates.
