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Efficient Shapley Values Estimation by Amortization for Text Classification

Chenghao Yang, Fan Yin, He He, Kai-Wei Chang, Xiaofei Ma, and Bing Xiang, in ACL, 2023.

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Despite the popularity of Shapley Values in explaining neural text classification models, computing them is prohibitive for large pretrained models due to a large number of model evaluations as it needs to perform multiple model evaluations over various perturbed text inputs. In practice, Shapley Values are often estimated stochastically with a smaller number of model evaluations. However, we find that the estimated Shapley Values are quite sensitive to random seeds¡Xthe top-ranked features often have little overlap under two different seeds, especially on examples with the longer input text. As a result, a much larger number of model evaluations is needed to reduce the sensitivity to an acceptable level. To mitigate the trade-off between stability and efficiency, we develop an amortized model that directly predicts Shapley Values of each input feature without additional model evaluation. It is trained on a set of examples with Shapley Values estimated from a large number of model evaluations to ensure stability. Experimental results on two text classification datasets demonstrate that, the proposed amortized model can estimate black-box explanation scores in milliseconds per sample in inference time and is up to 60 times more efficient than traditional methods.

Bib Entry

  title = {Efficient Shapley Values Estimation by Amortization for Text Classification},
  author = {Yang, Chenghao and Yin, Fan and He, He and Chang, Kai-Wei and Ma, Xiaofei and Xiang, Bing},
  year = {2023},
  presentation_id = {},
  booktitle = {ACL}

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