充分利用人工智能和大型语言模型,支持卫生科学图书馆的收藏开发
1iportillo@chapman.edu, Health Sciences Librarian, Director of Rinker Campus Library Services, Leatherby Libraries, Chapman University, Irvine, CA.
Journal of the Medical Library Association : JMLA
|February 20, 2025
概括
生成型人工智能模型对健康科学图书馆员来说是有前途的,但对于收藏开发来说还不够可靠. 它们可以补充差距分析,但在建议新标题时可能会产生不准确性.
科学领域:
- 卫生科学 图书馆 图书馆 图书馆
- 在信息科学中的人工智能信息科学.
背景情况:
- 对于健康科学图书馆来说,收藏的发展至关重要.
- 生成型人工智能为图书馆专业人士提供了潜在的工具.
研究的目的:
- 评估生成性人工智能模型在健康科学图书馆收藏开发中的有效性.
- 评估AI模型在识别学科差距和建议新资源方面的表现.
主要方法:
- 四个生成性AI模型 (ChatGPT 4.0,Google Gemini,Perplexity,Microsoft Copilot) 在六个月的时间里进行了测试.
- 使用了两个不同的提示:一个用于生成电子书标题,另一个用于识别收藏缺口.
主要成果:
- 人工智能模型在生成电子书标题时显示出不一致性和不准确性.
- 所有测试的模型都提供了有用的分析和准确的国会图书馆电话号码,用于识别主题差距.
- 在一些人工智能模型输出中注意到了不准确性和幻觉.
结论:
- 大型语言模型 (LLM) 目前作为健康科学集合开发的主要工具是不可靠的.
- 法律法规可以作为补充工具来分析收藏覆盖范围,并识别主题差距.
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