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Related Questions
- How do attention mechanisms contribute to the interpretability of language models?
- Can you explain the trade-offs between attention mechanisms, feature importance, and saliency maps in providing insights into input feature weights?
- Are attention mechanisms more suitable than feature importance and saliency maps for complex long-range dependencies in sequences?
- How do various attention mechanisms, such as self-attention, interactive attention, and local context attention, differ in focusing on relevant input features?
- In situations where input features are sparsely represented or multi-modal, which methods is more effective: feature importance, saliency maps, or attention mechanisms in highlighting important features?
- Can you elaborate on the role of regularization methods, such as weight decoupling and retraining, in improving the utility of attention mechanisms over the other methods?
- How do attention mechanisms interplay with other interpretability tools, such as LRP (Layer-wise Relevance Propagation) and influence functions, in highlighting contributions of input features?
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