feature attribution in transformers
**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.