Discovering Transformer Circuits via a Hybrid Attribution and Pruning Framework
This work addresses a scalability problem for researchers in mechanistic interpretability, offering an incremental improvement over existing methods.
The paper tackles the trade-off between speed and faithfulness in transformer circuit discovery by proposing a hybrid attribution and pruning (HAP) framework, which is 46% faster than baselines while maintaining circuit faithfulness and preserving key components like S-inhibition heads in tasks such as Indirect Object Identification.
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.