造型测量可以揭示人工智能著作权,但人类扎:比较人类和日本七个大型语言模型
Wataru Zaitsu1, Mingzhe Jin2, Shunichi Ishihara3
1Faculty of Psychology, Mejiro University, Tokyo, Japan.
PloS one
|October 27, 2025
概括
笔表分析可以有效地检测人工智能 (AI) 生成的文本. 然而,人类检测人工智能写作的能力有限,像ChatGPT (o1) 这样的先进模型经常被误认为是人类写的.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 人与计算机的交互
背景情况:
- 大型语言模型 (LLM) 的扩散需要方法来区分人工智能生成的内容和人类写作.
- 音标分析提供了对文本分析的定量方法,重点是语言特征.
研究的目的:
- 评估造型分析在检测人工智能 (AI) 产生的文本方面的潜力.
- 评估人类识别人工智能产生的内容的能力.
主要方法:
- 研究1:将人写的文本与7个LLM的350个文本进行比较,使用基于造型特征的多维缩放 (MDS) (短语模式,POS大图,函数单词单图).
- 研究2:对403名日本参与者进行了在线调查,以评估他们的AI检测能力和信心水平.
主要成果:
- 造型特征有效地区分了人类和人工智能生成的文本,集成的特征实现了完美的歧视.
- 人类人工智能检测能力通常是有限的;像ChatGPT (o1) 这样的高级LLM经常被误认为是人类写的.
- 与其他LLM相比,Llama3.1显示出明显的风度特征;随机森林分类器实现了99.8%的准确性.
结论:
- 造型测量分析是人工智能文本检测的强大方法.
- 人类对人工智能产生的内容的判断是不可靠的,受到肤浅的语言线索的影响.
- 这些发现对有关AI内容验证的政策,教育和营销产生了重大影响.
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