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GeneTuring tests GPT models in genomics.


ABSTRACT: Generative Pre-trained Transformers (GPT) are powerful language models that have great potential to transform biomedical research. However, they are known to suffer from artificial hallucinations and provide false answers that are seemingly correct in some situations. We developed GeneTuring, a comprehensive QA database with 600 questions in genomics, and manually scored 10,800 answers returned by six GPT models, including GPT-3, ChatGPT, and New Bing. New Bing has the best overall performance and significantly reduces the level of AI hallucination compared to other models, thanks to its ability to recognize its incapacity in answering questions. We argue that improving incapacity awareness is equally important as improving model accuracy to address AI hallucination.

SUBMITTER: Hou W 

PROVIDER: S-EPMC10054955 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Benchmarking large language models for genomic knowledge with GeneTuring.

Shang Xinyi X   Liao Xu X   Ji Zhicheng Z   Hou Wenpin W  

bioRxiv : the preprint server for biology 20250912


Large language models (LLMs) show promise in biomedical research, but their effectiveness for genomic inquiry remains unclear. We developed GeneTuring, a benchmark consisting of 16 genomics tasks with 1,600 curated questions, and manually evaluated 48,000 answers from ten LLM configurations, including GPT-4o (via API, ChatGPT with web access, and a custom GPT setup), GPT-3.5, Claude 3.5, Gemini Advanced, GeneGPT (both slim and full), BioGPT, and BioMedLM. A custom GPT-4o configuration integrated  ...[more]

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