大型语言模型时代的科学写作:人工智能与人类创建的内容的计算分析
Rohan Khera1,2,3,4, Aline F Pedroso1, Vipina K Keloth2
1Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT. (R.K., A.F.P.).
Stroke
|August 15, 2025
概括
大型语言模型 (LLM) 生成具有独特语言特征的科学文本,但这些特征并不总是可以被人类专家识别出来的. 人工智能检测工具对于保持科学完整性至关重要.
科学领域:
- 人工智能的人工智能
- 科学沟通科学沟通
- 计算语言学 计算语言学
背景情况:
- 大型语言模型 (LLM) 可以加速科学文献合成,但有可能传播错误信息.
- 将人工智能生成的科学文本与人类撰写的科学文本区分开来是一个越来越大的挑战.
研究的目的:
- 描述区分人工智能产生的和人类撰写的科学文本的语言特征.
- 评估AI检测工具在此任务中的性能.
主要方法:
- 34篇脑血管论文的计算合成 (12篇由人工智能生成,22篇由人类撰写).
- 专家评审由中风编辑委员会成员进行.
- 专家性能与GPTZero人工智能检测工具的比较.
- 语言特征提取:语法,语义,可读性,年级水平和困惑性.
主要成果:
- 人工智能生成的文本表现出较低的字数,复杂性,困惑性和可读性得分.
- 人类专家错误地分类了31.8%的人类撰写的文章.
- GPTZero准确地分类了100%的人工智能生成和95.5%的人类撰写的文章,但依赖于有限的文本部分.
结论:
- 士课程制作的科学内容与人类写作有可衡量的语言差异.
- 人类专家无法始终识别这些差异.
- 技术辅助的人工智能检测工具对于维护科学通信完整性至关重要.
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