Attention Analysis Limitations › What Attention Can and Cannot Tell You

Given these limitations, it is useful to be explicit about the appropriate and inappropriate uses of attention visualization. The goal is not to abandon attention analysis entirely but to calibrate the strength of the claims you draw from it.

Attention weights are appropriate for:

• Exploratory analysis: Forming hypotheses about what the model might be doing, to be tested with more rigorous methods
• Debugging structural issues: If a model attends only to [CLS] and [SEP] tokens uniformly, something is likely wrong with the training setup
• Educational demonstration: Showing students what the attention mechanism looks like in practice, with appropriate caveats about interpretation
• Qualitative sanity checks: Verifying that a model attends to relevant tokens at all, not that attention weights represent precise importances
• Anomaly detection: Identifying unusual attention patterns that might indicate distribution shift, adversarial inputs, or unexpected behavior worth investigating further

Attention weights are inappropriate for:

• Causal claims: Concluding that token $j$ caused the prediction because it received high attention
• Contrastive explanation: Explaining why the model chose class A over class B based on attention patterns
• Faithfulness assessment: Assuming that high attention means the model's decision depends heavily on that token
• Model comparison: Comparing two models based on their attention patterns, since different attention configurations can implement the same function
• Bias audits: Concluding that a model is or is not biased based on whether it attends to demographic attributes in the input

The last point about bias audits deserves emphasis. Several papers have attempted to detect bias in transformer models by examining whether attention is drawn to demographic attributes (race, gender, nationality) in text. If a model attends to "she" when making predictions about a nurse but "he" when making predictions about a doctor, the argument goes, attention reveals gender bias. But this reasoning conflates attention with causal influence. The model might attend to pronouns for grammatical agreement while making its prediction based entirely on the occupation noun and its associations in pretraining data. The attention pattern would show the grammatical function, not the bias.