Title: Topic-Based Watermarks for Large Language Models Show Chinese title

Title: 基于主题的水印用于大型语言模型

Abstract: The indistinguishability of Large Language Model (LLM) output from human-authored content poses significant challenges, raising concerns about potential misuse of AI-generated text and its influence on future AI model training. Watermarking algorithms offer a viable solution by embedding detectable signatures into generated text. However, existing watermarking methods often entail trade-offs among attack robustness, generation quality, and additional overhead such as specialized frameworks or complex integrations. We propose a lightweight, topic-guided watermarking scheme for LLMs that partitions the vocabulary into topic-aligned token subsets. Given an input prompt, the scheme selects a relevant topic-specific token list, effectively "green-listing" semantically aligned tokens to embed robust marks while preserving the text's fluency and coherence. Experimental results across multiple LLMs and state-of-the-art benchmarks demonstrate that our method achieves comparable perplexity to industry-leading systems, including Google's SynthID-Text, yet enhances watermark robustness against paraphrasing and lexical perturbation attacks while introducing minimal performance overhead. Our approach avoids reliance on additional mechanisms beyond standard text generation pipelines, facilitating straightforward adoption, suggesting a practical path toward globally consistent watermarking of AI-generated content. Show Chinese abstract

Abstract: 大型语言模型（LLM）输出与人工撰写的文本之间的不可区分性带来了重大挑战，引发了对AI生成文本潜在滥用及其对未来AI模型训练影响的担忧。 水印算法通过在生成的文本中嵌入可检测的签名提供了一个可行的解决方案。 然而，现有的水印方法通常在攻击鲁棒性、生成质量以及额外开销（如专用框架或复杂集成）之间存在权衡。 我们提出了一种轻量级、主题引导的LLM水印方案，该方案将词汇表划分为主题对齐的标记子集。 给定一个输入提示，该方案选择一个相关的主题特定标记列表，有效地“绿色列出”语义对齐的标记以嵌入鲁棒的水印，同时保持文本的流畅性和连贯性。 在多个LLM和最先进的基准测试中的实验结果表明，我们的方法在困惑度方面与行业领先系统（包括Google的SynthID-Text）相当，同时在对抗改写和词汇扰动攻击时增强了水印的鲁棒性，且引入了最小的性能开销。 我们的方法避免了对标准文本生成流程之外的额外机制的依赖，便于直接采用，表明了实现AI生成内容全球一致水印的实用路径。