A Survey on LLM Watermarking: Theory and Deployment
This work addresses the urgent need for reliable watermarking techniques to attribute and audit content generated by large language models, which is prone to misuse and difficult to trace. The authors propose a structured analytical framework for practical deployment, systematically categorizing existing watermarking methods along dimensions such as embedding timing, detection authority, underlying assumptions, and adversarial threats. By examining approaches spanning generation-time and training-time watermarks—including those based on sampling bias, encoding, representation, and training—the study evaluates their performance in terms of log accessibility, key management, and robustness. Effectiveness is further tested under realistic attacks like paraphrasing, translation, and style transfer. The analysis synthesizes strengths and limitations of prevailing schemes, offers a practical selection guide, and highlights emerging challenges such as cross-model transferability and multimodal integration.