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Related Questions
- Can attention mechanisms help in pinpointing the specific input tokens or words that contribute to a model's output, thereby improving error identification and debugging?
- How do attention weights or scores affect the interpretability of large language models, and what do they reveal about the model's reasoning process?
- In what ways do attention mechanisms facilitate the identification of biases in large language models, such as token or entity-level biases?
- Are there any limitations or challenges associated with using attention mechanisms for improving interpretability in large language models?
- Can attention mechanisms help in understanding how a model generalizes to unseen data or out-of-distribution samples, and what insights can be gained from this?
- How do attention mechanisms compare to other techniques, such as feature attribution methods or model interpretability techniques, in terms of their ability to improve model interpretability?
- Can attention mechanisms be used to identify potential sources of errors or biases in large language models, such as overfitting or underfitting, and how do they relate to model performance?
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