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Related Questions
- What are the typical challenges of interpreting feature importances in complex models such as text summarization?
- How can feature attribution methods like LIME struggle to identify biased feature importance in text summarization?
- What are some common pitfalls when using LIME for feature importance analysis in text summarization models?
- Can LIME's reliance on local approximations of the model's behavior lead to biased or incomplete feature importance rankings?
- How do issues like feature interaction, non-linearity, and high-dimensional data impact the effectiveness of LIME for identifying biased feature importance?
- What alternative or complementary methods can be used in conjunction with LIME to provide a more comprehensive understanding of biased feature importance?
- Are there specific scenarios or data types in text summarization where LIME may not be effective or even misleading for identifying biased feature importance?
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