区分GPT生成和人类编写的医学文本:定量研究
Wenxiong Liao1, Zhengliang Liu2, Haixing Dai2
1School of Computer Science and Engineering, South China University of Technology, Guangzhou, China.
JMIR medical education
|December 28, 2023
概括
像ChatGPT这样的大型语言模型生成类似人类的文本,但医疗内容需要验证. 机器学习模型可以有效地检测人工智能生成的医学文本,确保在医疗保健中可靠地使用人工智能.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 医疗信息学 医疗信息学
背景情况:
- 像ChatGPT这样的大型语言模型 (LLM) 可以产生类似人类的文本.
- 人工智能产生的内容的扩散需要验证,特别是在医学领域.
- 错误的医疗AI内容带来了错误信息和公众伤害的风险.
研究的目的:
- 分析人类专家和ChatGPT生成的医学文本之间的语言差异.
- 开发机器学习 (ML) 工作流程,以检测人工智能生成的医疗内容.
- 在医疗领域促进负责任的AI实施.
主要方法:
- 人类撰写和ChatGPT生成的医学文本的编译数据集.
- 分析语言特征,包括词汇,语音部分,情感和困惑.
- 设计和实施的ML模型,包括基于变压器的方法,用于文本分类.
主要成果:
- 人类医疗文本更具体,信息更丰富;聊天GPT文本优先流利和一般术语.
- 一个基于变压器的模型在检测ChatGPT生成的医疗文本时获得了超过95%的F1分数.
- 发布的数据集和代码有助于进一步的研究和开发.
结论:
- 人工智能生成的医疗文本的语言特征与人类专家写作有所不同.
- 拟议的ML算法可以可靠地检测ChatGPT生成的医学内容.
- 这项研究为医疗领域的负责任和值得信赖的AI应用铺平了道路.
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