Year: 2026
Type: conference-paper
Abstract: 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 XMARK, a novel method for encoding and decoding binary messages in LLM-generated texts.The unique design of 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 XMARK significantly improves decoding accuracy while preserving the quality of watermarked text, outperforming prior methods.
Authors: Jiahao Xu, Rui Hu, Olivera Kotevska, Zikai Zhang
Institutions: Oak Ridge National Laboratory, University of Nevada, Reno
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166809199