人工智能产生的同行评审的个人经验:一个案例研究
1Independent researcher, Marseille, France. nlovecchio@gmail.com.
Research integrity and peer review
|April 6, 2025
概括
本案例报告详细介绍了一位作者涉嫌人工智能产生的同行评审的经验,强调了关于科学出版中使用大语言模型 (LLM) 的期刊透明度和作者准备的需要.
科学领域:
- 科学出版科学出版
- 在学术界的人工智能.
- 学术传播学术交流
背景情况:
- 关于人工智能产生的同行评审的社会背景的研究有限,尽管对大型语言模型 (LLM) 使用的语料库级研究.
- 这篇文章介绍了第一人称收到同行评审报告的第一人称记录,该报告被怀疑是人工智能生成的.
研究的目的:
- 记录个人经验与可疑的AI产生的同行评价.
- 为面临类似情况的作者提供经验教训和建议.
- 倡导在同行评审中使用人工智能的透明政策.
主要方法:
- 案例报告详细介绍了一起涉及涉嫌人工智能产生的同行评价事件的时间表.
- 分析文本模式,人工智能检测工具和模拟以支持指控.
- 来自个人经验和日记互动的建议.
主要成果:
- 在使用人工智能的指控之后,与期刊进行了为期两个月的争议,导致手稿被撤回.
- 该期刊否认了伦理违规行为,而没有证实或否认LLM的使用.
- 作者建议作者积极参与有关人工智能政策和证据收集的期刊.
结论:
- 期刊必须实施透明的政策,要求在同行评审中披露大型语言模型 (LLM) 的使用情况.
- 开放的同行评审可能会减轻人工智能的滥用,但在人工智能时代,问责对所有各方至关重要.
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