使用大型语言模型来总结生物医学文章中的证据:人工智能和人类注释文献的探索性比较
Michelle Colder Carras1, Riaz Qureshi2, Kevin Naaman2
1Department of International Health, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD, 21205, United States, 1 410-955-3934.
JMIR formative research
|February 12, 2026
概括
使用ChatGPT的人工智能生成的注释可以快速概述科学文献,提供一致的质量和上下文,但可能存在不准确性和比人类注释更多的错误. 建议对人工智能生成的注释文献进行仔细的人类验证.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 科学沟通科学沟通
背景情况:
- 有注释的书目对于文献审查至关重要,但需要大量的人类专业知识和时间.
- 人工智能工具提供了有效总结的潜力,但可能会引入错误.
研究的目的:
- 评估使用ChatGPT生成注释文献的可行性.
- 将人工智能生成的注释与人类撰写的注释在准确性,简洁性和质量方面进行比较.
主要方法:
- 两个人类注释者和三个版本的ChatGPT (3.5,4,5) 独立注释了15个出版物.
- 标注被评估为字数,可读性 (Flesch阅读方便),捕捉主要点,存在错误,并包括质量/上下文.
- 统计模型被用来比较由人类和人工智能生成的注释.
主要成果:
- 聊天GPT注释比人类注释更长,更难读.
- 在捕捉人工智能和人类注释之间的主要要点方面没有发现显著差异.
- 人类注释的错误较少,而人工智能注释更一致地包括质量和上下文,尽管有时不准确.
结论:
- 像ChatGPT这样的AI工具可以有效地生成文献摘要,提供一致的质量和上下文.
- 人工智能生成的注释可能包含更多的错误和不准确性,需要人类验证.
- 需要对快速工程进行进一步的研究,以提高AI聊天机器人的性能,以总结科学文献.
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