Transformer Circuit Evaluation Metrics Are Not Robust
Research Area: Science of LMs
Keywords: mechanistic interpretability, interpretability, circuits
TL;DR: We show that the faithfulness metrics used to measure performance of circuits in previous work in mechanistic interpretability are not robust to slight changes in experimental setup.
Abstract: Mechanistic interpretability work attempts to reverse engineer the learned algorithms present inside neural networks. One focus of this work has been to discover 'circuits' - subgraphs of the full model that explain behaviour on specific tasks. But how do we measure the performance of such circuits? Prior work has attempted to measure circuit 'faithfulness' - the degree to which the circuit replicates the performance of the full model. In this work, we survey many considerations for designing experiments that measure circuit faithfulness by ablating portions of the model's computation. Concerningly, we find existing methods are highly sensitive to seemingly insignificant changes in the ablation methodology. We conclude that existing circuit faithfulness scores reflect _both_ the methodological choices of researchers as well as the actual components of the circuit - the task a circuit is required to perform depends on the ablation used to test it. The ultimate goal of mechanistic interpretability work is to understand neural networks, so we emphasize the need for more clarity in the precise claims being made about circuits. We open source a library at [this https URL](https://github.com/UFO-101/auto-circuit) that includes highly efficient implementations of a wide range of ablation methodologies and circuit discovery algorithms.
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Submission Number: 1313
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