@inproceedings{lee-etal-2023-xmd,
    title = "{XMD}: An End-to-End Framework for Interactive Explanation-Based Debugging of {NLP} Models",
    author = "Lee, Dong-Ho  and
      Kadakia, Akshen  and
      Joshi, Brihi  and
      Chan, Aaron  and
      Liu, Ziyi  and
      Narahari, Kiran  and
      Shibuya, Takashi  and
      Mitani, Ryosuke  and
      Sekiya, Toshiyuki  and
      Pujara, Jay  and
      Ren, Xiang",
    editor = "Bollegala, Danushka  and
      Huang, Ruihong  and
      Ritter, Alan",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-demo.25",
    doi = "10.18653/v1/2023.acl-demo.25",
    pages = "264--273",
    abstract = "NLP models are susceptible to learning spurious biases (i.e., bugs) that work on some datasets but do not properly reflect the underlying task. Explanation-based model debugging aims to resolve spurious biases by showing human users explanations of model behavior, asking users to give feedback on the behavior, thenusing the feedback to update the model. While existing model debugging methods have shown promise, their prototype-level implementations provide limited practical utility. Thus, we propose XMD: the first open-source, end-to-end framework for explanation-based model debugging. Given task- or instance-level explanations,users can flexibly provide various forms of feedback via an intuitive, web-based UI. After receiving user feedback, XMD automatically updates the model in real time, by regularizing the model so that its explanationsalign with the user feedback. The new model can then be easily deployed into real-world applications via Hugging Face. Using XMD, we can improve the model{'}s OOD performance on text classification tasks by up to 18{\%}.",
}

NLP models are susceptible to learning spurious biases (i.e., bugs) that work on some datasets but do not properly reflect the underlying task. Explanation-based model debugging aims to resolve spurious biases by showing human users explanations of model behavior, asking users to give feedback on the behavior, thenusing the feedback to update the model. While existing model debugging methods have shown promise, their prototype-level implementations provide limited practical utility. Thus, we propose XMD: the first open-source, end-to-end framework for explanation-based model debugging. Given task- or instance-level explanations,users can flexibly provide various forms of feedback via an intuitive, web-based UI. After receiving user feedback, XMD automatically updates the model in real time, by regularizing the model so that its explanationsalign with the user feedback. The new model can then be easily deployed into real-world applications via Hugging Face. Using XMD, we can improve the model{'}s OOD performance on text classification tasks by up to 18{\%}.
Lee, Dong-Ho Kadakia, Akshen Joshi, Brihi Chan, Aaron Liu, Ziyi Narahari, Kiran Shibuya, Takashi Mitani, Ryosuke Sekiya, Toshiyuki Pujara, Jay Ren, Xiang
XMD: An End-to-End Framework for Interactive Explanation-Based Debugging of NLP Models