Feature attribution in transformers is the set of methods that assign contribution scores from internal features to model outputs - it helps quantify which representations are most responsible for specific predictions.

What Is Feature attribution in transformers?

• Definition: Attribution maps output behavior to heads, neurons, tokens, or learned feature directions.
• Methods: Includes gradients, integrated gradients, patch-based scores, and decomposition approaches.
• Granularity: Can operate at token-position, component, or circuit level.
• Interpretation: Attribution values indicate influence but do not always imply full causality.

Why Feature attribution in transformers Matters

• Transparency: Provides interpretable summaries of model decision pathways.
• Debugging: Highlights surprising or spurious features driving incorrect outputs.
• Safety Analysis: Supports audits for bias, leakage, and policy-relevant behavior triggers.
• Model Editing: Identifies candidate features for targeted intervention.
• Evaluation: Enables systematic comparison of interpretability methods on common tasks.

How It Is Used in Practice

• Method Ensemble: Use multiple attribution methods to reduce single-method blind spots.
• Causal Follow-Up: Validate high-attribution features with intervention experiments.
• Prompt Diversity: Compute attribution across varied contexts to test feature stability.

Feature attribution in transformers is a central quantitative toolkit for interpreting transformer behavior - feature attribution in transformers is most actionable when paired with causal verification and robustness checks.