评估由使用ChatGPT的提示和未提示模型生成的生物医学布尔搜索字符串的质量:试点研究
Robyn B Reed1, Derek J Barnett2
1Harrell Health Sciences Library, Penn State College of Medicine, Biomedical Informatics and Emerging Technologies Librarian, 500 University Dr, Hershey, PA, USA.
Medical reference services quarterly
|December 18, 2024
概括
像ChatGPT这样的生成人工智能可以创建PubMed搜索字符串,但结果显示与发表的系统评论相比,相对应的重叠是适度的,不是完全的复制. 促使和未促使的AI方法显示出最小的差异.
科学领域:
- 图书统计学 图书统计学
- 研究中的人工智能.
- 信息科学 信息科学 信息科学
背景情况:
- 系统审查依赖于全面的文献搜索.
- PubMed是生物医学文献的主要数据库.
- 布尔式搜索字符串对于有效的数据库查询至关重要.
研究的目的:
- 为了评估生成AI (ChatGPT) 创建PubMed布尔搜索字符串.
- 将人工智能生成的搜索字符串与已发布的搜索字符串的有效性进行比较.
- 确定在搜索字符串形成中使用ChatGPT的最佳方法.
主要方法:
- 一项试点研究将ChatGPT生成的布尔字符串的搜索结果与已发表的系统性审查结果进行了比较.
- 测试了两种方法:提示和未提示的ChatGPT查询.
- 获取的出版物的重叠被量化.
主要成果:
- 通过人工智能生成和发布的搜索字符串检索的出版物之间观察到适度的重叠.
- 人工智能生成的字符串不能完全复制已发布的字符串的输出.
- 在提示和未提示的ChatGPT使用之间,在有效性上几乎没有明显的差异.
结论:
- 生成型人工智能显示了协助PubMed.的布尔搜索字符串创建的潜力.
- 当前的人工智能能力提供了适度的,但不完全的,复制既定的搜索策略.
- 需要进一步的研究来完善人工智能对系统审查搜索的方法.
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