验证链减少大型语言模型中的虚构

摘要

在大型语言模型中，生成看似正确但实际错误的事实信息，被称为幻觉，是一个尚未解决的问题。我们研究语言模型在纠正错误时思考其回答的能力。我们开发了“验证链”（CoVe）方法，模型首先（i）起草初始回答；然后（ii）计划验证问题以核实起草内容；（iii）独立回答这些问题，以避免受其他回答的影响；最后（iv）生成最终经过验证的回答。在实验中，我们展示了CoVe在各种任务中减少幻觉的效果，包括来自Wikidata的基于列表的问题、闭卷MultiSpanQA和长文本生成。

English

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.

验证链减少大型语言模型中的虚构

Chain-of-Verification Reduces Hallucination in Large Language Models

摘要

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