Chain-of-Verification Reduces Hallucination in Large Language Models
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.
650bc20187dcda6616ad4cf6
Shehzaad Dhuliawala
admin_assigned
2023-09-21T09:47:03.315Z
6325db12ddb9ff68f4698292
/avatars/88e9f57a17bc9b41ddad508e7b1d2640.svg
Shehzaad Dhuliawala
user
shehzaadzd
650bc20187dcda6616ad4cf7
Mojtaba Komeili
claimed_verified
2024-06-27T09:49:28.487Z
659d717f2dbf5c8fee9c1fac
/avatars/6bea1195499a66da9b48fe0c27781ba8.svg
Mojtaba Komeili
user
mkomeili
650bc20187dcda6616ad4cf8
Jing Xu
650bc20187dcda6616ad4cf9
Roberta Raileanu
admin_assigned
2023-09-21T09:48:20.397Z
633e94793a17ab61de8e2b9c
/avatars/5f2f58ddeed211393660ada6b135f0d5.svg
Roberta Raileanu
user
rraileanu
650bc20187dcda6616ad4cfa
Xian Li
650bc20187dcda6616ad4cfb
Asli Celikyilmaz
650bc20187dcda6616ad4cfc
Jason Weston
admin_assigned
2023-09-21T09:49:37.891Z
62f023a36a027498eaa2f9cc
/avatars/8ac1c5c74d0957e3c6cc94b3a7795c37.svg
Jason Weston
user
spermwhale
Chain-of-Verification Reduces Hallucination in Large Language Models
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.
The Chain-of-Verification method reduces hallucinations in language models by having them draft, plan verification questions, answer independently, and then generate a final verified response.
Chain-of-Verification CoVe hallucinations language models verification questions MultiSpanQA longform text generation