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Related Questions
- What are the key differences between SHAP and LIME in bias detection for text-based summarization models?
- How do SHAP and LIME compare to other attribution methods such as DeepLIFT and Grad-CAM in text-based summarization models?
- What are the strengths and weaknesses of SHAP and LIME in detecting biases in text-based summarization models compared to other methods?
- How do SHAP and LIME handle out-of-distribution or adversarial samples in text-based summarization models?
- Can SHAP and LIME be used to detect biases in text-based summarization models that are specific to certain demographics or subgroups?
- How do SHAP and LIME compare to model-agnostic bias detection methods such as equality of opportunity and equality of odds?
- What are the computational costs and resources required to implement SHAP and LIME for bias detection in text-based summarization models compared to other methods?
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