聊天GPT生成的内容和相似性指数在化学†
1Bankura Sammilani College, Bankura, West Bengal 722102, India.
Journal of chemical information and modeling
|August 22, 2023
概括
这项研究评估了ChatGPT的化学内容相似性,在多种抄袭检测工具中发现了令人惊的低匹配率. 这项研究分析了20个化学子学科,以评估人工智能生成的文本原创性.
科学领域:
- 化学 化学 化学
- 人工智能的人工智能
- 学术诚信 学术诚信
背景情况:
- 在学术环境中越来越多地使用像ChatGPT这样的AI工具,需要对其输出进行评估.
- 评估人工智能产生的内容的原创性对于保持科学学科的学术标准至关重要.
- 之前的研究并没有广泛关注人工智能生成的化学内容的相似性指数.
研究的目的:
- 为了确定ChatGPT在化学领域产生的内容的相似度指数.
- 调查人工智能生成的化学内容是否可以通过标准抄袭检测软件轻松识别为非原创.
- 提供对人工智能生成的科学文本原创性的见解.
主要方法:
- 在化学中选择了20个不同的子学科,以杜威十进制分类,西尔斯列表和国会图书馆学科标题 (LCSH) 等受控词汇工具为指导.
- 聊天GPT用于为每个选定的子主题生成内容.
- 生成的内容使用三个著名的抄袭检测平台进行了相似性分析:iThenticate,Urkund和Turnitin.
主要成果:
- 该研究发现,在所有测试的抄袭检测工具中,ChatGPT生成的化学内容的匹配率惊人地低.
- 分析显示,人工智能生成的化学文本没有触发高相似度得分,表明某种程度的明显原创性.
- 观察到相似度得分的变化取决于具体的化学子对象和使用的检测工具.
结论:
- 在化学中,ChatGPT生成的内容在通过传统的抄袭检测软件评估时具有相对较低的相似度指数.
- 这些发现表明,当前的AI模型可能会产生不易被标记为抄袭的文本,这对学术完整性监测构成了挑战.
- 建议进行进一步的研究,以探索人工智能产生的科学内容的细微差别,并开发更有效的检测方法.
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