@inproceedings{tafjord-etal-2022-entailer,
    title = "Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning",
    author = "Tafjord, Oyvind  and
      Dalvi Mishra, Bhavana  and
      Clark, Peter",
    editor = "Goldberg, Yoav  and
      Kozareva, Zornitsa  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.emnlp-main.134/",
    doi = "10.18653/v1/2022.emnlp-main.134",
    pages = "2078--2093",
    abstract = "Our goal is a question-answering (QA) system that can show how its answers are implied by its own internal beliefs via a systematic chain of reasoning. Such a capability would allow better understanding of why a model produced the answer it did. Our approach is to recursively combine a trained backward-chainingmodel, capable of generating a set of premises entailing an answer hypothesis, with a verifier that checks that the model itself believes those premises (and the entailment itself) through self-querying. To our knowledge, this is the first system to generate multistep chains that are both faithful (the answer follows from the reasoning) and truthful (the chain reflects the system{'}s own internal beliefs). In evaluation using two different datasets, users judge that a majority (70{\%}+) of generated chains clearly show how an answer follows from a set of facts - substantially better than a high-performance baseline - while preserving answer accuracy. By materializing model beliefs that systematically support an answer, new opportunities arise for understanding the model{'}s system of belief, and diagnosing and correcting its misunderstandings when an answer is wrong."
}