arXiv:2505.17630v4 Announce Type: replace Abstract: Circuit localization methods aim to identify the subset of model components responsible for specific behaviors in large language models, enabling detailed mechanistic analysis. Most existing methods assume components act independently and estimate importance by perturbing each component in isolation. However, components in neural networks interact, and ignoring these interactions leads to systematic misestimation of component importance. We find that one particularly problematic interaction is attention self-repair, in which softmax redistrib
This research addresses a known limitation in AI interpretability by proposing a method that accounts for interaction effects, which is crucial as AI models become more complex and their internal workings more opaque.
Improving the accuracy of circuit localization helps understand how large language models function, facilitating better debugging, safety analysis, and the development of more reliable AI systems.
The proposed 'Interaction-Aware Backpropagation' method allows for more precise identification of responsible model components, leading to a more robust understanding of AI behavior than previous isolation-based approaches.
- · AI researchers
- · ML engineers
- · AI safety organizations
- · AI development platforms
- · Developers relying solely on isolation-based interpretability methods
More accurate understanding of specific cognitive functions within large language models.
Accelerated development of more robust, explainable, and potentially safer AI architectures.
Enhanced trust and adoption of AI systems due to improved transparency and auditability, potentially influencing regulation.
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Read at arXiv cs.CL