Multi-bit watermarking has emerged as a promising solution for embedding imperceptible binary messages into Large Language Model (LLM)-generated text, enabling reliable attribution and tracing of malicious usage of LLMs. Despite recent progress, existing methods still face key limitations: some become computationally infeasible for large messages, while others suffer from a poor trade-off between text quality and decoding accuracy. Moreover, the decoding accuracy of existing methods drops significantly when the number of tokens in the generated text is limited, a condition that frequently arises in practical usage. To address these challenges, we propose \textsc{XMark}, a novel method for encoding and decoding binary messages in LLM-generated texts. The unique design of \textsc{XMark}'s encoder produces a less distorted logit distribution for watermarked token generation, preserving text quality, and also enables its tailored decoder to reliably recover the encoded message with limited tokens. Extensive experiments across diverse downstream tasks show that \textsc{XMark} significantly improves decoding accuracy while preserving the quality of watermarked text, outperforming prior methods. The code is at https://github.com/JiiahaoXU/XMark.

翻译：多比特水印技术能够将不可察觉的二进制信息嵌入大语言模型（LLM）生成的文本中，为LLM的恶意使用提供可靠的归因与追踪能力，因此成为一种具有前景的解决方案。尽管近期研究取得进展，现有方法仍面临关键局限：部分方法在大信息量场景下计算不可行，另一些方法则在文本质量与解码精度之间难以取得良好平衡。此外，当生成文本的令牌数量受限时（这在实际应用中频繁出现），现有方法的解码准确率显著下降。针对上述挑战，我们提出XMark——一种在LLM生成文本中编码与解码二进制信息的新型方法。XMark编码器的独特设计可产生失真度更低的水印令牌生成logit分布，从而保障文本质量；同时，其定制化解码器能够在有限令牌数量下可靠恢复编码信息。跨多个下游任务的广泛实验表明，XMark在保持水印文本质量的同时显著提升了解码精度，性能优于先前方法。代码见https://github.com/JiiahaoXU/XMark。