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Our goal is a question-answering (QA) system that can show how its answersare implied by its own internal beliefs via a systematic chain of reasoning.Such a capability would allow better understanding of why a model produced theanswer it did. Our approach is to recursively combine a trainedbackward-chaining model, capable of generating a set of premises entailing ananswer hypothesis, with a verifier that checks that the model itself believesthose premises (and the entailment itself) through self-querying. To ourknowledge, this is the first system to generate multistep chains that are bothfaithful (the answer follows from the reasoning) and truthful (the chainreflects the system's own internal beliefs). In evaluation using two differentdatasets, users judge that a majority (70%+) of generated chains clearly showhow an answer follows from a set of facts - substantially better than ahigh-performance baseline - while preserving answer accuracy. By materializingmodel beliefs that systematically support an answer, new opportunities arisefor understanding the model's system of belief, and diagnosing and correctingits misunderstandings when an answer is wrong.

Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning