Related papers: Chain-of-Verification Reduces Hallucination in Large Language Models

Chain-of-Verification Reduces Hallucination in Large Language Models

URL: http://arxiv.org/abs/2309.11495v2
Date: Mon, 25 Sep 2023 15:25:49 GMT
Title: Chain-of-Verification Reduces Hallucination in Large Language Models
Authors: Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, Roberta Raileanu, Xian Li, Asli Celikyilmaz, Jason Weston
Abstract summary: We study the ability of language models to deliberate on the responses they give in order to correct their mistakes. We develop the Chain-of-Verification (CoVe) method whereby the model first drafts an initial response. We show CoVe decreases hallucinations across a variety of tasks, from list-based questions from Wikidata to closed book MultiSpanQA.
Score: 80.99318041981776
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Generation of plausible yet incorrect factual information, termed hallucination, is an unsolved issue in large language models. We study the ability of language models to deliberate on the responses they give in order to correct their mistakes. We develop the Chain-of-Verification (CoVe) method whereby the model first (i) drafts an initial response; then (ii) plans verification questions to fact-check its draft; (iii) answers those questions independently so the answers are not biased by other responses; and (iv) generates its final verified response. In experiments, we show CoVe decreases hallucinations across a variety of tasks, from list-based questions from Wikidata, closed book MultiSpanQA and longform text generation.

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