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Related Questions
- What are some common challenges in interpreting the outputs of text summarization models, and how do model-agnostic techniques address these challenges?
- How do model-agnostic techniques, such as feature importance and partial dependence plots, account for the context-dependent nature of text summarization models?
- Can you explain how model-agnostic interpretability techniques, like SHAP values, handle the complex interactions between input text features and the generated summaries?
- In what ways do model-agnostic techniques, such as LIME, provide insights into the meaning and intent behind the input text that influences the generated summaries?
- How do model-agnostic interpretability techniques, like permutation feature importance, account for the non-linear relationships between input text features and the generated summaries?
- Can you describe how model-agnostic techniques, such as anchor points, help to identify the key input text features that contribute to the generated summaries?
- What are some advantages and limitations of using model-agnostic interpretability techniques for understanding the context-dependent nature of text summarization models?
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