Attention Analysis Limitations › Practical Implications for Model Analysis
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For practitioners doing actual model analysis, these findings suggest a layered approach to interpretability that uses the most appropriate tool for each question.
Start with gradient-based methods. Integrated Gradients or SHAP values provide more principled attribution scores with theoretical guarantees. They are slower to compute than attention but far more reliable. For task-critical applications like medical diagnosis support or legal reasoning assistance, the additional computation time is worthwhile to get attributions with stronger theoretical grounding.
Use attention for pattern exploration, not causal conclusions. If integrated gradients show a token and attention also shows it, that is meaningful convergent evidence. Two independent methods agreeing on the importance of a token provides stronger support than either method alone. If only attention shows it, treat it as a hypothesis to test with gradient-based methods.
Be specific about what you are measuring. "Attention weights from layer 6, head 4, averaged over the last 100 examples for the [CLS] token" is a meaningful quantity. "The model's attention" is not. The lack of specificity in how attention is extracted and aggregated is one of the most common sources of unreliable claims in interpretability research.
Compare methods. When attention and gradient-based methods disagree, take note. The disagreement itself is informative: it tells you that the model's computation is more complex than the attention pattern suggests. Systematic disagreements across multiple inputs might indicate that a specific head is performing a computation that the gradient-based method is missing, or that the gradient-based method is capturing a signal that attention is not routing explicitly.
Consider the architecture. Cross-attention in generation models, induction heads, and other architecturally constrained mechanisms are more interpretable via attention than feed-forward-dominated computations. When the computation you care about must pass through attention, attention is the right measurement tool.