评估生成人工智能在获取信息方面的表现,与手动策划的遗传和基因组数据对比
Elly Poretsky1, Victoria C Blake1,2, Carson M Andorf3,4
1Crop Improvement and Genetics Research Unit, United States Department of Agriculture-Agricultural Research Service, Western Regional Research Center, 800 Buchanan St, Albany, CA 94710, United States.
Database : the journal of biological databases and curation
|February 18, 2025
概括
像GPT-4这样的大型语言模型 (LLM) 在从科学论文中提取遗传数据方面表现有前途,有助于策展人. 在小麦和大麦遗传学研究的准确性和数据提取方面,GPT-4的表现优于GPT-3.5.
科学领域:
- 遗传学和基因组学 遗传学和基因组学
- 生物信息学是一种生物信息学.
- 科学中的人工智能.
背景情况:
- 集中式的生物数据库依赖于精准的数据来保证准确性.
- 手动数据整理是耗时的,需要专门的专业知识.
- 大型语言模型 (LLM) 提供了从科学文献中提取信息的自动化潜力.
研究的目的:
- 评估生成预训练变压器 (GPT) -3.5和GPT-4在提取和呈现小麦和大麦遗传学研究数据中的性能.
- 在数据提取和总结方面,比较LLM与人类策展人的有效性.
- 评估LLM驱动系统在协助生物数据库管理员方面的潜力.
主要方法:
- 一套36篇关于小麦和大麦遗传学的期刊文章由专业策展人策划.
- 开发了一个基于GPT的检索增强生成问答系统.
- 使用GPT-3.5和GPT-4来回答有关特征和定量特征位置 (QTL) 的问题.
- 使用基于GPT的数据框架代理来过和总结精选的数据.
主要成果:
- 在97%的时间里,GPT-4正确地对手稿进行了分类,并提取了80%的特征和61%的标记特征关联.
- 在整个基因组中,GPT-4检索了高达91%的与疾病相关的QTL,在特定区域检索了96%.
- 在大多数任务中,GPT-4的表现始终优于GPT-3.5,幻觉较少.
结论:
- LLM,特别是GPT-4,显示出有很大的潜力,可以帮助策展人提取和呈现生物信息.
- 人类和计算策展人可以协同工作,提高效率和数据可访问性.
- 用户必须意识到LLM的局限性,包括潜在的不准确性和不完整的信息提取.
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