使用写作风格揭示ChatGPT文本
Lamia Berriche1, Souad Larabi-Marie-Sainte1
1College of Computer & Information Sciences, Prince Sultan University, Saudi Arabia.
Heliyon
|July 10, 2024
概括
这项研究引入了一种新的方法,使用风度特征来检测人工智能生成的文本,特别是来自ChatGPT. 拟议的XGBoost分类器在识别ChatGPT抄袭方面实现了100%的准确性,超过了现有的技术.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 像ChatGPT这样的大型语言模型 (LLM) 的扩散导致了人工智能生成文本的使用增加.
- 人们担心人工智能文本生成器有可能破坏学术完整性和标准.
- 检测人工智能生成的内容对于保持学术诚实至关重要.
研究的目的:
- 开发和评估一种技术,用于检测人工智能生成的文本中的抄袭行为,特别是那些由ChatGPT制作的文本.
- 评估各种机器学习分类器在区分人类和人工智能写作风格方面的有效性.
- 与TF-IDF等传统技术相比,提出一种优越的特征提取方法.
主要方法:
- 从文档中提取和规范化内在的造型特征.
- 实现和比较经典分类器 (k-最近邻居,决策树,天真贝叶斯) 和集体分类器 (XGBoost,堆叠).
- 严格评估使用交叉验证,超参数调整和多次训练代.
主要成果:
- 经典和集体学习分类器都在区分人类和ChatGPT写作风格方面表现出有效性.
- XGBoost 分类器在检测人工智能生成文本的准确性,回忆和精确性方面取得了完美的分数 (100%).
- 提出的造型特征提取方法超过了TF-IDF技术,在相同的数据集上获得了最先进的结果.
- 集体分类器以98%的准确度正确识别了混合的人类-AI文本.
- 段落级的作者归因达到92.3%的准确性.
结论:
- 拟议的风度特征提取和XGBoost分类方法在检测ChatGPT生成的抄袭方面非常有效.
- 该方法为识别文档中人工智能生成的内容和混合作者提供了强大的解决方案.
- 这种技术为学术机构提供了宝贵的工具,以维护AI时代的完整性.
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