Towards Teachable Reasoning Systems

Our goal is a teachable reasoning system for question-answering (QA), where a user can interact with faithful answer explanations, and correct errors so that the system improves over time. Our approach is three-fold: First, generated chains of reasoning show how answers are implied by the system's own internal beliefs. Second, users can interact with the explanations to identify erroneous model beliefs and provide corrections. Third, we augment the model with a dynamic memory of such corrections. Retrievals from memory are used as additional context for QA, to help avoid previous mistakes in similar new situations - a novel type of memory-based continuous learning. To our knowledge, this is the first system to generate chains that are both faithful (the answer follows from the reasoning) and truthful (the chain reflects the system's own beliefs, as ascertained by self-querying). In evaluation, users judge that a majority (65%+) of generated chains clearly show how an answer follows from a set of facts - substantially better than a high-performance baseline. We also find that using simulated feedback, our system (called EntailmentWriter) continually improves with time, requiring feedback on only 25% of training examples to reach within 1% of the upper-bound (feedback on all examples). We observe a similar trend with real users. This suggests new opportunities for using language models in an interactive setting where users can inspect, debug, correct, and improve a system's performance over time.