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Related Questions
- Can saliency maps accurately capture the complex interactions between linguistic and semantic features in text summarization?
- Do saliency maps provide sufficient information to understand the decision-making process of LLMs in text summarization, or do they oversimplify it?
- How do saliency maps handle the issue of feature correlations and multicollinearity in text data?
- Can saliency maps be used to identify biases in the decision-making process of LLMs in text summarization?
- How do saliency maps perform on tasks that require long-range dependencies or contextual understanding, such as text summarization?
- Can saliency maps be used to compare the decision-making processes of different LLMs or models in text summarization?
- What are some potential sources of error or noise in saliency maps that could affect their interpretability and reliability?
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