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Related Questions
- What is the SHAP (SHapley Additive exPlanations) value and how is it used in the field of machine learning?
- How do SHAP values help explain the contribution of individual words to the output of a text summarization model?
- Can you provide an example of how SHAP values are calculated and interpreted in the context of a text summarization model?
- What are the benefits of using SHAP values to understand the contribution of individual words in a text summarization model?
- How do SHAP values compare to other explainability techniques, such as LIME (Local Interpretable Model-agnostic Explanations)?
- Can SHAP values be used to identify biased or unfair contributions of individual words in a text summarization model?
- How do SHAP values impact the development of more transparent and accountable text summarization models?
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