  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, then using 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 explanations align 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%.
