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Related Questions
- What are the key differences between permutation importance and SHAP values in attributing bias in text summarization models?
- How does permutation importance compare to feature importance methods like LIME and TreeExplainer in detecting biases in text summarization models?
- Can permutation importance be used to identify biases in text summarization models that are caused by specific words or phrases?
- How does permutation importance handle the issue of model interpretability in text summarization, where the input is a complex sequence of words?
- What are the advantages and disadvantages of using permutation importance compared to other methods like sensitivity analysis and model-agnostic interpretability methods?
- Can permutation importance be used to detect biases in text summarization models that are caused by the order of words in the input sequence?
- How does permutation importance compare to other model-agnostic interpretability methods like Anchors and Partial Dependence Plots in detecting biases in text summarization models?
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