Functional Faithfulness in the Wild: Circuit Discovery with Differentiable Computation Graph Pruning

• URL: http://arxiv.org/abs/2407.03779v1
• Date: Thu, 4 Jul 2024 09:42:25 GMT
• Title: Functional Faithfulness in the Wild: Circuit Discovery with Differentiable Computation Graph Pruning
• Authors: Lei Yu, Jingcheng Niu, Zining Zhu, Gerald Penn,
• Abstract summary: We introduce a comprehensive reformulation of the task known as Circuit Discovery, along with DiscoGP. DiscoGP is a novel and effective algorithm based on differentiable masking for discovering circuits.
• Score: 14.639036250438517
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we introduce a comprehensive reformulation of the task known as Circuit Discovery, along with DiscoGP, a novel and effective algorithm based on differentiable masking for discovering circuits. Circuit discovery is the task of interpreting the computational mechanisms of language models (LMs) by dissecting their functions and capabilities into sparse subnetworks (circuits). We identified two major limitations in existing circuit discovery efforts: (1) a dichotomy between weight-based and connection-edge-based approaches forces researchers to choose between pruning connections or weights, thereby limiting the scope of mechanistic interpretation of LMs; (2) algorithms based on activation patching tend to identify circuits that are neither functionally faithful nor complete. The performance of these identified circuits is substantially reduced, often resulting in near-random performance in isolation. Furthermore, the complement of the circuit -- i.e., the original LM with the identified circuit removed -- still retains adequate performance, indicating that essential components of a complete circuits are missed by existing methods. DiscoGP successfully addresses the two aforementioned issues and demonstrates state-of-the-art faithfulness, completeness, and sparsity. The effectiveness of the algorithm and its novel structure open up new avenues of gathering new insights into the internal workings of generative AI.

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