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.

翻译：生成看似合理但实际错误的事实信息（即所谓的“幻觉”）是大型语言模型中尚未解决的问题。我们研究了语言模型通过反思自身输出以纠正错误的能力。为此，我们提出了链式验证（Chain-of-Verification，CoVe）方法：模型首先（i）起草初始回答；然后（ii）规划验证问题以核查其草稿；（iii）独立回答这些问题，避免答案受其他回复的干扰；最后（iv）生成经验证的最终回答。实验表明，CoVe在多种任务中均能有效降低幻觉，包括基于维基数据的列表式问答、闭卷多跨度问答（MultiSpanQA）以及长文本生成任务。