Causal Head Gating: A Framework for Interpreting Roles of Attention Heads in Transformers

• URL: http://arxiv.org/abs/2505.13737v1
• Date: Mon, 19 May 2025 21:24:13 GMT
• Title: Causal Head Gating: A Framework for Interpreting Roles of Attention Heads in Transformers
• Authors: Andrew Nam, Henry Conklin, Yukang Yang, Thomas Griffiths, Jonathan Cohen, Sarah-Jane Leslie,
• Abstract summary: We present causal head gating (CHG), a scalable method for interpreting the functional roles of attention heads in transformer models.<n>CHG learns soft gates over heads and assigns them a causal taxonomy based on their impact on task performance.<n>We show that CHG scores yield causal - not merely correlational - insight, validated via ablation and causal mediation analyses.
• Score: 3.7132788234059104
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present causal head gating (CHG), a scalable method for interpreting the functional roles of attention heads in transformer models. CHG learns soft gates over heads and assigns them a causal taxonomy - facilitating, interfering, or irrelevant - based on their impact on task performance. Unlike prior approaches in mechanistic interpretability, which are hypothesis-driven and require prompt templates or target labels, CHG applies directly to any dataset using standard next-token prediction. We evaluate CHG across multiple large language models (LLMs) in the Llama 3 model family and diverse tasks, including syntax, commonsense, and mathematical reasoning, and show that CHG scores yield causal - not merely correlational - insight, validated via ablation and causal mediation analyses. We also introduce contrastive CHG, a variant that isolates sub-circuits for specific task components. Our findings reveal that LLMs contain multiple sparse, sufficient sub-circuits, that individual head roles depend on interactions with others (low modularity), and that instruction following and in-context learning rely on separable mechanisms.

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