GeneTuring在基因组学中测试GPT模型。

Wenpin Hou, Xinyi Shang, Zhicheng Ji
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摘要

生成预训练转换器(GPT)是功能强大的语言模型,在生物医学研究领域具有巨大的潜力。然而,众所周知,他们会产生人为幻觉,并在某些情况下提供看似正确的错误答案。我们开发了GeneTuring,这是一个包含600个基因组学问题的综合QA数据库,并手动为包括GPT-3、ChatGPT和New Bing在内的六个GPT模型返回的10800个答案打分。与其他模型相比,新冰的整体性能最好,并显著降低了人工智能幻觉的水平,这要归功于它能够识别自己在回答问题时的无能。我们认为,提高丧失能力意识与提高模型准确性以解决人工智能幻觉同样重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Benchmarking large language models for genomic knowledge with GeneTuring.

Large language models have demonstrated great potential in biomedical research. However, their ability to serve as a knowledge base for genomic research remains unknown. We developed GeneTuring, a comprehensive Q&A database containing 1,200 questions in genomics, and manually scored 25,200 answers provided by six GPT models, including GPT-4o, Claude 3.5, and Gemini Advanced. GPT-4o with web access showed the best overall performance and excelled in most tasks. However, it still failed to correctly answer all questions and may not be fully reliable for providing answers to genomic inquiries.

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