在人类编辑下,对大型语言模型的水印进行稳健检测
Xiang Li1, Feng Ruan2, Huiyuan Wang1
1University of Pennsylvania.
概括
一种新方法,即Truncated Goodness-of-Fit (Tr-GoF) 测试,即使在人类编辑后,也能有效地检测水印文字. 这种强大的方法克服了现有方法的局限性,改善了大型语言模型 (LLM) 文本检测.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 信息安全 信息安全
背景情况:
- 水印对于区分人工智能生成的文本和人类写作至关重要.
- 人类编辑大大降低了现有的水印检测准确度.
- 当前的方法与文本修改带来的噪音作斗争.
研究的目的:
- 引入一种新的方法,即Truncated Goodness-of-Fit (Tr-GoF) 测试,用于强大的水印检测.
- 解决在大量人为编辑下检测水印文本的挑战.
- 提高识别人工智能生成内容的可靠性.
主要方法:
- 模拟的人类编辑使用混合模型检测.
- 开发了Tr-GoF测试,该测试基于一个截断的好适合性方法.
- 分析了测试的非对称最佳性和检测效率.
主要成果:
- 经过验证的Tr-GoF在重大编辑下实现了对Gumbel-max水印的最佳稳定检测.
- 证明了Tr-GoF的自适应性,不需要精确的编辑级别或LLM规格.
- 显示Tr-GoF在中度修改方案中实现了高检测效率.
- 强调了基于总和的检测规则对编辑引起的噪音的不足.
结论:
- Tr-GoF测试在检测人类编辑的水印文本方面提供了卓越的稳定性和效率.
- "Tr-GoF"的性能优于现有的基于总和的方法,特别是在有重大文本修改的情况下.
- 经验结果验证了Tr-GoF在各种LLM (如OPT和LLaMA) 上的有效性.
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