评论员经验检测和判断人类与人工智能之间的差异 内容:中风杂志论文竞赛
Gisele S Silva1, Rohan Khera2,3,4, Lee H Schwamm3,5,6
1Hospital Israelita Albert Einstein and Departamento de Neurologia e Neurocirurgia, Universidade Federal de São Paulo, Brazil (G.S.S.).
Stroke
|September 3, 2024
概括
人类和人工智能 (AI) 大型语言模型 (LLM) 产生了科学论文. 评论员努力将人工智能与人类作者区分开来,对人工智能论文的作文评分较高,但对人工智能生成的内容有偏见.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 科学出版科学出版
背景情况:
- 大型语言模型 (LLM) 生成类似人类的文本和图像.
- 法律学士能够制作有说服力的科学论文进行同行评审的能力尚不清楚.
- 评估人工智能对科学作者和质量认知的影响至关重要.
研究的目的:
- 在一个盲目的竞争中评估人类和人工智能生成的科学论文.
- 测量评论员对文章质量,说服力和作者身份的看法.
- 为了确定人工智能作者是否可以被专家审稿人准确地识别.
主要方法:
- 在2024年的论文竞赛中,人类作者和4个不同的法学士就有争议的中风护理主题进行了论文竞赛.
- 38 卒中 编辑委员会成员 (血管神经病学家) 盲目对作者身份进行评价 34 篇文章.
- 评论员评估了文章的质量,说服力,主题最佳和作者类型 (人与人工智能).
主要成果:
- 人类和人工智能论文获得了类似的整体评分,但人工智能论文在作文质量方面得分更高.
- 审稿人只能在50%的时间内准确识别作者类型;之前的LLM经验提高了准确性.
- 说服力与评审者将AI作为作者类型 (aOR,1.53;P=0.01) 联系在一起.
结论:
- 专家评论员很难区分人类和人工智能生成的科学论文.
- 观察到对人工智能生成的文章有偏见,特别是在"主题最佳"评级方面.
- 期刊应该教育评论员在科学写作中对AI进行教育,并制定明确的AI作者权政策.
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