Chain-of-Verification Reduces Hallucination in Large Language Models (2024.findings-acl)
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Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, Roberta Raileanu, Xian Li, Asli Celikyilmaz, Jason Weston
| Challenge: | Large language models can generate plausible but incorrect factual information, termed hallucinations, but they can still fail on lesser known facts. |
| Approach: | They develop a method that allows language models to deliberate on the responses they give in order to correct their errors. |
| Outcome: | The proposed method decreases hallucinations across a variety of tasks, including list-based questions, closed book MultiSpanQA and longform text generation. |
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