Detecting hallucinations in large language models using semantic entropy.

ABSTRACT: Large language model (LLM) systems, such as ChatGPT¹ or Gemini², can show impressive reasoning and question-answering capabilities but often 'hallucinate' false outputs and unsubstantiated answers^3,4. Answering unreliably or without the necessary information prevents adoption in diverse fields, with problems including fabrication of legal precedents⁵ or untrue facts in news articles⁶ and even posing a risk to human life in medical domains such as radiology⁷. Encouraging truthfulness through supervision or reinforcement has been only partially successful⁸. Researchers need a general method for detecting hallucinations in LLMs that works even with new and unseen questions to which humans might not know the answer. H

SUBMITTER: Farquhar S 

PROVIDER: S-EPMC11186750 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature