使用深度变压器模型检测和分类ChatGPT生成的内容.
Mahdi Maktabdar Oghaz1, Lakshmi Babu Saheer1, Kshipra Dhame1
1Faculty of Science and Engineering, Anglia Ruskin University, Cambridge, United Kingdom.
Frontiers in artificial intelligence
|April 21, 2025
概括
这项研究开发了先进的机器学习模型,以准确检测人工智能生成的文本,在区分人类和人工智能写作方面实现了高性能. 这些发现提供了一种可靠的方法来打击AI在学术界及其他领域的滥用.
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.
- 机器学习 机器学习
背景情况:
- 人工智能的快速发展,特别是神经网络和像ChatGPT这样的大型语言模型 (LLM),使复杂的文本生成成为可能.
- 越来越多的人工智能生成的文本引发了对学术完整性,错误信息和隐私的担忧.
- 目前用于检测人工智能产生的内容的现有方法经常显示出可疑的性能,特别是在先进的LLM中.
研究的目的:
- 开发和评估用于准确检测和分类人工智能生成文本的机器学习模型.
- 建立一个强大的基准来区分人类撰写的内容和人工智能生成的内容.
- 为了减轻AI文本生成工具的潜在滥用.
主要方法:
- 编译了一个由人类编写和人工智能生成 (ChatGPT) 文本的数据集.
- 训练和评估各种机器学习和深度学习模型,重点是基于变压器的架构.
- 使用F1分数和准确性等指标评估模型有效性.
主要成果:
- 一个定制的基于RoBERTa的深度学习模型实现了F1得分为0.992,准确度为0.991.
- DistilBERT表现出强的表现,F1得分为0.998,准确度为0.988.
- 这些结果表明,在检测人工智能生成的内容方面,它具有特殊的能力.
结论:
- 开发的模型提供了一种可靠的方法来区分人工智能生成的文本和人类写作.
- 这项研究为AI文本检测奠定了坚实的基础,这对于解决学术完整性和错误信息至关重要.
- 未来的工作应该集中在各种AI来源的模型通用性和不断变化的检测挑战上.
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