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arXiv · 2407.13594

Validating Mechanistic Interpretations: An Axiomatic Approach

Nils Palumbo·Ravi Mangal·Zifan Wang·Saranya Vijayakumar·Corina S. Pasareanu·Somesh Jha
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Abstract

Mechanistic interpretability aims to reverse engineer the computation performed by a neural network in terms of its internal components. Although there is a growing body of research on mechanistic interpretation of neural networks, the notion of a mechanistic interpretation itself is often ad-hoc. Inspired by the notion of abstract interpretation from the program analysis literature that aims to develop approximate semantics for programs, we give a set of axioms that formally characterize a mechanistic interpretation as a description that approximately captures the semantics of the neural network under analysis in a compositional manner. We demonstrate the applicability of these axioms for validating mechanistic interpretations on an existing, well-known interpretability study as well as on a new case study involving a Transformer-based model trained to solve the well-known 2-SAT problem.

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Nils Palumbo, Ravi Mangal, Zifan Wang, Saranya Vijayakumar, Corina S. Pasareanu, Somesh Jha. 2024-07-18. Validating Mechanistic Interpretations: An Axiomatic Approach. https://arxiv.org/abs/2407.13594

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