标题： 针对大语言模型的多数位感知水印技术 显示英文标题

标题： Majority Bit-Aware Watermarking For Large Language Models

摘要： 大型语言模型（LLMs）在现实应用中的广泛部署引发了对其潜在滥用生成有害或欺骗性内容的担忧。 为解决这一问题，水印技术作为一种有前景的解决方案出现，通过在生成的文本中嵌入可识别的二进制信息，用于来源验证和滥用追踪。 尽管近期研究探索了能够嵌入丰富信息（如用户标识符）的多比特水印方案，但它们通常面临文本质量和解码准确性之间的基本权衡：为了确保可靠的消息解码，它们必须在编码过程中限制首选标记集合的大小，而这种限制会降低生成内容的质量。 在本工作中，我们提出了MajorMark，一种通过多数位感知编码改进这一权衡的新水印方法。 MajorMark根据消息的多数位选择首选标记集合，从而实现更大且更灵活的标记采样。 与之前依赖标记频率分析进行解码的方法不同，MajorMark采用基于聚类的解码策略，在首选标记集合较大时仍能保持高解码准确性，从而同时保持内容质量和解码准确性。 我们进一步引入了MajorMark$^+$，该方法将消息分成多个块，独立编码并确定性地解码每个块，从而进一步提高水印文本的质量并提升解码准确性。 在最先进的LLMs上的大量实验表明，我们的方法显著提高了解码准确性和文本生成质量，优于之前的多比特水印基线。 显示英文摘要

摘要： The growing deployment of Large Language Models (LLMs) in real-world applications has raised concerns about their potential misuse in generating harmful or deceptive content. To address this issue, watermarking techniques have emerged as a promising solution by embedding identifiable binary messages into generated text for origin verification and misuse tracing. While recent efforts have explored multi-bit watermarking schemes capable of embedding rich information such as user identifiers, they typically suffer from the fundamental trade-off between text quality and decoding accuracy: to ensure reliable message decoding, they have to restrict the size of preferred token sets during encoding, yet such restrictions reduce the quality of the generated content. In this work, we propose MajorMark, a novel watermarking method that improves this trade-off through majority bit-aware encoding. MajorMark selects preferred token sets based on the majority bit of the message, enabling a larger and more flexible sampling of tokens. In contrast to prior methods that rely on token frequency analysis for decoding, MajorMark employs a clustering-based decoding strategy, which maintains high decoding accuracy even when the preferred token set is large, thus preserving both content quality and decoding accuracy. We further introduce MajorMark$^+$, which partitions the message into multiple blocks to independently encode and deterministically decode each block, thereby further enhancing the quality of watermarked text and improving decoding accuracy. Extensive experiments on state-of-the-art LLMs demonstrate that our methods significantly enhance both decoding accuracy and text generation quality, outperforming prior multi-bit watermarking baselines.