The Nonlinear Library AF - AtP*: An efficient and scalable method for localizing LLM behaviour to components by Neel Nanda
Mar 18, 2024
01:30
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: AtP*: An efficient and scalable method for localizing LLM behaviour to components, published by Neel Nanda on March 18, 2024 on The AI Alignment Forum.
Authors: János Kramár, Tom Lieberum, Rohin Shah, Neel Nanda
A new paper from the Google DeepMind mechanistic interpretability team, from core contributors János Kramár and Tom Lieberum
Tweet thread summary, paper
Abstract:
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.
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Authors: János Kramár, Tom Lieberum, Rohin Shah, Neel Nanda
A new paper from the Google DeepMind mechanistic interpretability team, from core contributors János Kramár and Tom Lieberum
Tweet thread summary, paper
Abstract:
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.
Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.