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Our goal is a teachable reasoning system for question-answering (QA), where auser can interact with faithful answer explanations, and correct its errors sothat the system improves over time. Our approach is to augment a QA model witha dynamic memory of user feedback, containing user-supplied corrections toerroneous model beliefs that users identify during interaction. Retrievals frommemory are used as additional context for QA, to help avoid previous mistakesin similar new situations - a novel application of memory-based continuouslearning. With simulated feedback, we find that our system (called TeachMe)continually improves with time, and without model retraining, requiringfeedback on only 25% of training examples to reach within 1% of the upper-bound(feedback on all examples). Similarly, in experiments with real users, weobserve a similar trend, with performance improving by over 15% on a hiddentest set after teaching. This suggests new opportunities for using frozenlanguage models in an interactive setting where users can inspect, debug, andcorrect the model's beliefs, leading to improved system's performance overtime.

Towards Teachable Reasoning Systems: Using a Dynamic Memory of User Feedback for Continual System Improvement