attention visualization

Attention visualization displays attention weights to understand what the model focuses on during prediction. **What attention shows**: Which input tokens/positions influence each output position, relationship patterns across sequence, layer-by-layer information routing. **Visualization types**: Heatmaps (query-key attention matrices), head views (compare attention heads), token-level highlighting, attention flow diagrams. **Tools**: BertViz (interactive visualization), Ecco, Weights & Biases attention plotting, custom matplotlib heatmaps. **Interpretation caveats**: **Attention ≠ importance**: High attention doesn't mean causal influence on output. **Not faithful**: Attention may not reflect underlying reasoning process. **Many heads**: Patterns vary across heads - which to examine? **Use cases**: Debugging specific predictions, finding syntactic patterns (heads attending to previous token, subject-verb, etc.), qualitative analysis, presentations. **Better alternatives**: Attribution methods, probing, activation patching provide more causal evidence. **Best practices**: Use as exploratory tool, don't over-interpret, combine with other interpretability methods, focus on consistent patterns. Starting point for understanding but not definitive explanation.