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Interpreting Transformers Through Attention Head Intervention

Mason Kadem, Rong Zheng

TL;DR

This work tackles mechanistic interpretability of transformers by adopting attention head intervention as a causal methodology. It distinguishes plausibility from faithfulness and formalizes criteria for faithful explanations, demonstrating that targeted head ablation can reveal which heads causally drive behavior. Across studies, heads show both specialization and substantial redundancy, with few heads essential for performance and many removable without large losses, enabling model compression and controlled interventions. The findings support using head-level causality to both understand and steer model behavior, including practical safety applications like reducing toxicity without retraining. The approach highlights a path from interpretation to principled intervention, with implications for reliability, safety, and future multimodal architectures.

Abstract

Neural networks are growing more capable on their own, but we do not understand their neural mechanisms. Understanding these mechanisms' decision-making processes, or mechanistic interpretability, enables (1) accountability and control in high-stakes domains, (2) the study of digital brains and the emergence of cognition, and (3) discovery of new knowledge when AI systems outperform humans.

Interpreting Transformers Through Attention Head Intervention

TL;DR

This work tackles mechanistic interpretability of transformers by adopting attention head intervention as a causal methodology. It distinguishes plausibility from faithfulness and formalizes criteria for faithful explanations, demonstrating that targeted head ablation can reveal which heads causally drive behavior. Across studies, heads show both specialization and substantial redundancy, with few heads essential for performance and many removable without large losses, enabling model compression and controlled interventions. The findings support using head-level causality to both understand and steer model behavior, including practical safety applications like reducing toxicity without retraining. The approach highlights a path from interpretation to principled intervention, with implications for reliability, safety, and future multimodal architectures.

Abstract

Neural networks are growing more capable on their own, but we do not understand their neural mechanisms. Understanding these mechanisms' decision-making processes, or mechanistic interpretability, enables (1) accountability and control in high-stakes domains, (2) the study of digital brains and the emergence of cognition, and (3) discovery of new knowledge when AI systems outperform humans.
Paper Structure (37 sections, 3 equations, 2 figures)

This paper contains 37 sections, 3 equations, 2 figures.

Table of Contents

  1. Introduction
  2. Taxonomy
  3. Transformer Architecture Background
  4. Self-Attention Mechanism
  5. Multi-Head Attention
  6. Why Attention Seemed Interpretable
  7. The Need for Causal Evidence
  8. The Focus of This Paper
  9. The Attention Explanation Debate
  10. Early Attention Visualization Studies
  11. Challenges to Attention as Explanation
  12. Different Attention Patterns Can Produce Identical Outputs
  13. Weak Correlation Between Attention and Importance
  14. High-Attention Heads Can Have Little Functional Impact
  15. Plausibility versus Faithfulness
  16. ...and 22 more sections

Figures (2)

  • Figure 1: A high-level taxonomy of interpretability approaches. We note several important caveats. These categories are not mutually exclusive, and the same technique may serve different purposes depending on application. For example, attention visualization can be purely observational or part of mechanistic analysis. Techniques can also be distinguished by explanation type. Attention head ablation provides counterfactual explanations (what would happen without this head), while attention visualization primarily provides factual explanations (what did the model attend to), though it can be used counterfactually when comparing patterns across interventions. This paper focuses on attention head intervention (mechanistic) while examining attention visualization (observational) to trace the debate about faithful explanations.
  • Figure 2: Interpretability approaches. Left: Inherent (transparent by design). Center: Post-hoc (external explainer tools). Right: Mechanistic (reverse-engineering through ablation).