arXiv cs.AI· Hyojun Kim, Hyeseon An, Yo-Sub Han·· 12 小时前AI 评分29
用学习式前瞻与均衡词带为扩散语言模型添加水印
Watermarking Diffusion Language Models with Learned Foresight and Balanced Bands
AI 导读
研究提出为扩散语言模型设计水印的新方法，学习两个此前固定的组件：作为绿名单的词汇带和预测候选词如何影响邻近预测的前瞻预测器，并按检测器方式对每个不同上下文-词对只计一次绿词。
正文 · 原文
Abstract:Green-red watermarks favor tokens from a green list that a secret key derives from the preceding tokens. Diffusion language models, however, often choose a token before its preceding tokens, and the token later shapes its neighbors' green lists. Prior work therefore tilts each choice to raise the expected number of green tokens at its own and its neighbors' positions. Building on this objective, we first learn two components that prior work keeps fixed: vocabulary bands that serve as green lists, separate competing candidates and keep the null distribution exact, and a foresight predictor that estimates how each candidate shifts its neighbors' predictions. Second, we count green tokens as the detector does, once per distinct context-token pair, so repetition earns nothing. On WaterBench with LLaDA-8B-Instruct, each learned component raises the z-score at equal distortion, and their gains are nearly additive. With a strong watermark, ours detects at least 96% of responses at a 1% false-positive rate while matching the LLM-judge score of unwatermarked text. Ours also detects more responses on Dream and as many or more on code and question answering without a significant loss in task accuracy.
| Comments: | 31 pages, 7 figures. Code: this https URL |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.32205 [cs.AI] |
| (or arXiv:2610.32205v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.32205 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Hyojun Kim [view email]
[v1]
Fri, 23 Oct 2026 05:58:58 UTC (310 KB)
来源：arXiv cs.AI · arxiv.org