@inproceedings{dhuliawala-etal-2024-chain,
title = "Chain-of-Verification Reduces Hallucination in Large Language Models",
author = "Dhuliawala, Shehzaad and
Komeili, Mojtaba and
Xu, Jing and
Raileanu, Roberta and
Li, Xian and
Celikyilmaz, Asli and
Weston, Jason",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.212",
pages = "3563--3578",
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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<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.</abstract>
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%0 Conference Proceedings
%T Chain-of-Verification Reduces Hallucination in Large Language Models
%A Dhuliawala, Shehzaad
%A Komeili, Mojtaba
%A Xu, Jing
%A Raileanu, Roberta
%A Li, Xian
%A Celikyilmaz, Asli
%A Weston, Jason
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F dhuliawala-etal-2024-chain
%X 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.
%U https://aclanthology.org/2024.findings-acl.212
%P 3563-3578
Markdown (Informal)
[Chain-of-Verification Reduces Hallucination in Large Language Models](https://aclanthology.org/2024.findings-acl.212) (Dhuliawala et al., Findings 2024)
ACL
- Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, Roberta Raileanu, Xian Li, Asli Celikyilmaz, and Jason Weston. 2024. Chain-of-Verification Reduces Hallucination in Large Language Models. In Findings of the Association for Computational Linguistics ACL 2024, pages 3563–3578, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.