Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning

Oyvind Tafjord, Bhavana Dalvi Mishra, Peter Clark


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.
Anthology ID:
2022.emnlp-main.134
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2078–2093
Language:
URL:
https://aclanthology.org/2022.emnlp-main.134
DOI:
Bibkey:
Cite (ACL):
Oyvind Tafjord, Bhavana Dalvi Mishra, and Peter Clark. 2022. Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 2078–2093, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning (Tafjord et al., EMNLP 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.emnlp-main.134.pdf