Chain-of-Verification Reduces Hallucination in Large Language Models
This addresses the issue of generating incorrect factual information in LLMs, which is a critical problem for users relying on accurate AI-generated content, and represents a novel method for a known bottleneck.
The paper tackles the problem of hallucination in large language models by proposing the Chain-of-Verification method, which reduces hallucinations across tasks such as Wikidata list-based questions, MultiSpanQA, and longform text generation.
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.