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bioRxiv · 10.1101/2023.03.11.532238

GeneTuring tests GPT models in genomics

Hou, W.·Ji, Z.
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Abstract

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 with NCBI APIs, developed in this study as SeqSnap, achieved the best overall performance. GPT-4o with web access and GeneGPT demonstrated complementary strengths. Our findings highlight both the promise and current limitations of LLMs in genomics, and emphasize the value of combining LLMs with domain-specific tools for robust genomic intelligence. GeneTuring offers a key resource for benchmarking and improving LLMs in biomedical research. Biographical NoteDr. Wenpin Hou is an Assistant Professor (tenure-track) in the Department of Biostatistics at Columbia University and member of its Data Science Institute, developing AI and statistical methods for decoding gene regulatory programs from single-cell and spatial multiomics data.

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Hou, W., Ji, Z.. 2023-03-13. GeneTuring tests GPT models in genomics. https://doi.org/10.1101/2023.03.11.532238

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