我们能发现由ChatGPT{-3.5, -4) 产生的虚假公众评论吗? : 日本的造型分析揭示了通过一次性学习创造的仿真
Wataru Zaitsu1, Mingzhe Jin2, Shunichi Ishihara3
1Faculty of Psychology, Mejiro University, Tokyo, Japan.
PloS one
|March 13, 2024
概括
这项研究表明,日本的风度分析可以区分人写和人工智能生成的公开评论. 机器学习模型准确地识别假评论,保护公民话语免受人工智能驱动的虚假信息的影响.
科学领域:
- 计算语言学 计算语言学
- 自然语言处理自然语言处理.
- 人工智能伦理学 人工智能伦理学
背景情况:
- 人工智能的兴起,就像ChatGPT一样,使大规模的虚假信息产生成为可能,包括为政府规则制定提供虚假公开评论.
- 将人工智能生成的文本与人类撰写的内容区分开来,对于维护公共话语和政策制定的完整性至关重要.
研究的目的:
- 开发和评估在日语中区分人类生成和ChatGPT生成的公众评论的方法.
- 评估造型分析和机器学习分类器在识别人工智能产生的虚假信息方面的有效性.
主要方法:
- 研究1:多维缩放 (MDS) 分析,将人类评论与GPT-3.5/GPT-4在零射击 (GPTzero) 和一射击 (GPTone) 学习条件下生成的文本进行比较.
- 研究2:随机森林 (RF) 分类器在集成的造型特征 (短语模式,POS大字母/三字母,函数词) 上受训,以区分人类,GPTzero和GPTone文本.
主要成果:
- 在人类评论和GPTzero文本之间,MDS揭示了明显的日本造型特征;GPTone文本在造型上更接近人类评论.
- 射频分类器在人类评论中达到~90%的精度,在人工智能生成的评论中达到99.5%的精度 (GPTzero和GPTone).
结论:
- 日本的风度分析在区分人类和人工智能生成的公众评论方面是有效的.
- 目前人工智能生成的虚假公开评论可以使用先进的文本分析技术可靠地从人类撰写的评论中进行歧视.
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