saliency maps

Saliency maps highlight which input tokens most influence the model output through gradient-based attribution. **Technique**: Compute gradient of output with respect to input embeddings, magnitude indicates importance (high gradient = small change causes large output change). **Methods**: Simple gradient (vanilla), Gradient × Input (element-wise product), Integrated Gradients (path from baseline to input), SmoothGrad (average over noisy inputs). **Interpretation**: High saliency tokens are important for prediction - but can be positive or negative influence. **Advantages**: Model-agnostic within differentiable models, no additional training, fast computation. **Limitations**: **Gradient saturation**: Low gradient doesn't mean unimportant. **Faithfulness**: May not reflect actual model reasoning. **Baseline dependence**: Integrated gradients require baseline choice. **For NLP**: Apply to embedding space, aggregate across embedding dimensions. **Tools**: Captum (PyTorch), TensorFlow Explainability, custom gradient computation. **Visualization**: Highlight tokens by saliency score, color intensity. **Comparison to attention**: Saliency is attribution (which inputs matter), attention is mechanism (how info flows). Useful diagnostic but interpret cautiously.