Chain-of-Verification Reduces Hallucination in Large Language Models

Citations Over TimeTop 1% of 2024 papers

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 Multi-SpanQA and longform text generation.

Related Papers

• → STKVS: secure technique for keyframes-based video summarization model(2024)7 cited
• Study and Two Types of Typical Usage of DataGrid Web Server Control(2005)
• Using DataGrid Control to Realize DataBase of Querying in VB6.0(2000)
• Susquehanna Chorale Spring Concert "Roots and Wings"(2017)
• → ИСПОЛЬЗОВAНИЕ ПОТЕНЦИAЛA СОЦИAЛЬНЫХ ПAРТНЕРОВ В ПОДГОТОВКЕ БУДУЩИХ ПЕДAГОГОВ(2024)