arXiv：cs.CL· Jiahao Xu, Rui Hu, Olivera Kotevska, Zikai Zhang·· 16 小时前
面向大语言模型的多数比特感知水印方法
Majority Bit-Aware Watermarking For Large Language Models
AI 导读
研究提出多数比特感知编码（majority bit-aware encoding）范式，让水印信号强度不再受限于绿名单（green list）大小，从而可在使用大绿名单时仍保留强水印信号。
正文
Abstract:The growing deployment of Large Language Models (LLMs) has raised concerns about their misuse in generating harmful or deceptive content. To address this issue, watermarking methods have been proposed to embed identifiable multi-bit messages into generated text for misuse tracing. However, existing methods often suffer from a fundamental trade-off between text quality and decoding accuracy. In particular, they have to restrict the size of the preferred token set (i.e., green list) during encoding to maintain a detectable watermark signal for decoding, which inevitably degrades generation quality. To improve this trade-off, we propose a novel message encoding paradigm called \textit{majority bit-aware encoding}, which relaxes the watermark signal strength from the green list size. This strategy allows for a strong watermark signal to be preserved in generated texts even when using a large green list. We introduce two instantiations of this paradigm: MajorMark and MajorMark$^{+}$, where the latter is specifically optimized for long messages. Extensive experiments on state-of-the-art LLMs demonstrate that our methods achieve higher decoding accuracy and superior text quality compared to prior baselines. The code is at this https URL.
| Comments: | This work has been accepted for presentation at NeurIPS 2026 |
| Subjects: | Computation and Language (cs.CL); Cryptography and Security (cs.CR) |
| Cite as: | arXiv:2508.03829 [cs.CL] |
| (or arXiv:2508.03829v3 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2508.03829 arXiv-issued DOI via DataCite |
Submission history
From: Jiahao Xu [view email]
[v1]
Tue, 5 Aug 2025 18:19:00 UTC (712 KB)
[v2]
Fri, 8 May 2026 22:08:12 UTC (900 KB)
[v3]
Tue, 13 Oct 2026 21:51:12 UTC (899 KB)
来源：arXiv：cs.CL · arxiv.org