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X-Mark: Towards Lossless Watermarking Through Lexical Redundancy

2023-11-16 11:58:31
Liang Chen, Yatao Bian, Yang Deng, Shuaiyi Li, Bingzhe Wu, Peilin Zhao, Kam-fai Wong


Text watermarking has emerged as an important technique for detecting machine-generated text. However, existing methods can severely degrade text quality due to arbitrary vocabulary partitioning, which disrupts the language model's expressiveness and impedes textual coherence. To mitigate this, we introduce XMark, a novel approach that capitalizes on text redundancy within the lexical space. Specifically, XMark incorporates a mutually exclusive rule for synonyms during the language model decoding process, thereby integrating prior knowledge into vocabulary partitioning and preserving the capabilities of language generation. We present theoretical analyses and empirical evidence demonstrating that XMark substantially enhances text generation fluency while maintaining watermark detectability. Furthermore, we investigate watermarking's impact on the emergent abilities of large language models, including zero-shot and few-shot knowledge recall, logical reasoning, and instruction following. Our comprehensive experiments confirm that XMark consistently outperforms existing methods in retaining these crucial capabilities of LLMs.

Abstract (translated)

文本水印作为一种重要的方法,用于检测由机器生成的文本。然而,现有的方法会严重破坏文本质量,因为随意词汇分割会破坏语言模型的表现力并阻碍文本的连贯性。为了减轻这种破坏,我们引入了XMark,一种新方法,它利用词汇空间中的文本冗余来 capitalize。具体来说,XMark 在语言模型解码过程中引入了互斥规则来处理同义词,从而将先验知识融入词汇分割中并保留语言生成能力。我们提供了理论分析和实证证据,证明 XMark 在保持水印检测的同时显著增强文本生成流畅度。此外,我们研究了水印对大型语言模型新兴能力的影响,包括零 shots和少数 shot 知识召回、推理和指令跟随。我们的全面实验证实,XMark 持续优于现有方法,保留 LLM 的关键功能。



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