大型语言模型的水标的统计框架:枢纽,检测效率和最佳规则
Xiang Li1, Feng Ruan2, Huiyuan Wang1
1University of Pennsylvania.
概括
本研究引入了一个新的框架,用于在大型语言模型 (LLM) 文本中创建和检测水印. 该框架优化了水印检测,提高了识别人工智能生成内容的准确性.
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) 生成的文本需要方法来区分它和人类写作.
- 水印,将统计信号嵌入到LLM文本中,是检测人工智能生成内容的关键技术.
研究的目的:
- 引入一个通用和灵活的框架来评估水标统计效率.
- 为LLM生成的文本水标设计强大的检测规则.
主要方法:
- 该框架使用假设测试来检测水印.
- 它涉及选择一个关键统计数据和一个秘密密钥来控制假阳性率.
- 关闭形式的表达式为非对称的假负率是为了评估检测规则的功率而衍生出来的.
主要成果:
- 该框架将最佳检测规则的确定减少到最小化优化问题.
- 该框架应用于两个代表性水标,为水标实施提供了重要的发现.
- 理论上得出的最佳检测规则在数值实验中表现出与现有方法相比具有竞争力或优异的性能.
结论:
- 开发的框架提供了一个原则性的方法来设计和分析LLM文本水印.
- 从这个框架中获得的最佳检测规则在区分人工智能生成的文本方面提供了更高的准确性.
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