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NLP models are susceptible to learning spurious biases (i.e., bugs) that workon some datasets but do not properly reflect the underlying task.Explanation-based model debugging aims to resolve spurious biases by showinghuman users explanations of model behavior, asking users to give feedback onthe behavior, then using the feedback to update the model. While existing modeldebugging methods have shown promise, their prototype-level implementationsprovide limited practical utility. Thus, we propose XMD: the first open-source,end-to-end framework for explanation-based model debugging. Given task- orinstance-level explanations, users can flexibly provide various forms offeedback via an intuitive, web-based UI. After receiving user feedback, XMDautomatically updates the model in real time, by regularizing the model so thatits explanations align with the user feedback. The new model can then be easilydeployed into real-world applications via Hugging Face. Using XMD, we canimprove the model's OOD performance on text classification tasks by up to 18%.

XMD: An End-to-End Framework for Interactive Explanation-Based Debugging of NLP Models