Language Models Hallucinate, but May Excel at Fact Verification

arXiv:2310.14564v2 Announce Type: replace
Abstract: Recent progress in natural language processing (NLP) owes much to remarkable advances in large language models (LLMs). Nevertheless, LLMs frequently "hallucinate," resulting in non-factual outputs. Our carefully-designed human evaluation substantiates the serious hallucination issue, revealing that even GPT-3.5 produces factual outputs less than 25% of the time. This underscores the importance of fact verifiers in order to measure and incentivize progress. Our systematic investigation affirms that LLMs can be repurposed as effective fact verifiers with …

abstract advances arxiv cs.cl evaluation excel gpt gpt-3 gpt-3.5 hallucination human issue language language models language processing large language large language models llms natural natural language natural language processing nlp processing progress type verification