百大文章的识别和分类以及大型语言模型的未来:利用图书法分析进行主题分析
Ethan Bernstein1, Anya Ramsamooj1, Kelsey L Millar2
1College of Medicine, California Northstate University, Elk Grove, CA, United States.
JMIR AI
|August 27, 2025
概括
大型语言模型 (LLM) 的研究正在迅速推进. 医学在有影响力的LLM出版物中领先,但人工智能分类需要人类监督以获得准确性和减轻偏见.
科学领域:
- 图书计量和人工智能
- 信息科学
- 学术研究趋势
背景情况:
- 像ChatGPT这样的大型语言模型 (LLM) 的普及激发了广泛的学术研究.
- 研究涉及多个领域,包括医学,教育和技术,研究LLM的能力和影响.
研究的目的:
- 确定和分类去年最有影响力的LLM学术作品.
- 评估人工智能工具 (如ChatGPT) 在学术研究分类中的准确性.
- 为了比较人工智能和人类领导的研究分类.
主要方法:
- 通过Web of Science对最多引用的100篇LLM论文进行了图书统计分析.
- 根据领域,期刊,作者和研究类型对论文进行手动分类.
- 对ChatGPT-4的分类性能与人类专家审查的比较分析.
主要成果:
- 医学领域占主导地位 (43%),其次是教育 (26%) 和技术 (15%).
- 医学研究重点是临床应用,医疗保健中的AI局限性和医学教育.
- 人工智能分类显示对领域的认同很高 (92%),但对研究类型的认同较低 (47%).
结论:
- 在研究分类方面,LLM具有显著的潜力.
- 人类监督对于解决人工智能的局限性至关重要,
- 持续的道德评估和人工智能系统的改进对于最大限度地提高LLM的好处至关重要.
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