提出混合方法加速神经网络电路发现，兼顾速度与准确性。
Discovering Transformer Circuits via a Hybrid Attribution and Pruning Framework
- 先用快速定位高潜力子图，再通过剪枝提取真实电路。
- 比基线快46%且不降低电路忠实度。
- 适合研究大模型机制可解释性的人群使用。
解释语言模型常依赖电路分析，旨在识别完成特定任务的稀疏子网络（即电路）。现有算法面临根本权衡：属性打补丁法速度快但对完整模型不忠实，边剪枝法忠实但计算成本高。本文提出混合属性与剪枝（HAP）框架，利用属性打补丁识别高潜力子图，再通过边剪枝从中提取忠实电路。实验表明，HAP比基线算法快46%，且不牺牲电路忠实度。在间接宾语识别任务的案例研究中，该方法保留了协作性电路组件（如S抑制头），而属性打补丁法在高稀疏度下会将其剪除。结果表明，HAP能有效提升大规模模型机制可解释性研究的可扩展性。代码已公开于https://anonymous.4open.science/r/HAP-circuit-discovery。
原文摘要 · Abstract (English)
Interpreting language models often involves circuit analysis, which aims to identify sparse subnetworks, or circuits, that accomplish specific tasks. Existing circuit discovery algorithms face a fundamental trade-off: attribution patching is fast but unfaithful to the full model, while edge pruning is faithful but computationally expensive. This research proposes a hybrid attribution and pruning (HAP) framework that uses attribution patching to identify a high-potential subgraph, then applies edge pruning to extract a faithful circuit from it. We show that HAP is 46\% faster than baseline algorithms without sacrificing circuit faithfulness. Furthermore, we present a case study on the Indirect Object Identification task, showing that our method preserves cooperative circuit components (e.g. S-inhibition heads) that attribution patching methods prune at high sparsity. Our results show that HAP could be an effective approach for improving the scalability of mechanistic interpretability research to larger models. Our code is available at https://anonymous.4open.science/r/HAP-circuit-discovery.
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