# Watermarking Techniques for Large Language Models: A Survey

**Authors:** Yuqing Liang, Jiancheng Xiao, Wensheng Gan, Philip S. Yu  
**arXiv:** [2409.00089](https://arxiv.org/abs/2409.00089)  
**Date:** 2024-08-26  
**Summary page:** https://www.summarizepaper.com/en/paper/2409.00089v1/

## Key points

- Authors Yuqing Liang, Jiancheng Xiao, Wensheng Gan, and Philip S. Yu explore the field of artificial intelligence technology and its application in large language models (LLMs).
- LLMs are crucial for enhancing production, creativity, learning, and work efficiency across various domains.
- The potential harm from misuse of LLMs is addressed through proposing LLM watermarking techniques for intellectual property protection and traceability of generated multimedia data.
- The preprint provides a comprehensive review of LLM watermarking technology by examining historical development, current research landscape, advantages, limitations, and emerging multimodal capabilities.
- The authors aim to inspire novel applications by integrating traditional digital watermarking methods with advancements in LLM watermarking technology.
- The preprint offers valuable insights into challenges faced by current watermarking technologies and future prospects in this field.

## Summary

In their preprint titled "Watermarking Techniques for Large Language Models: A Survey," authors Yuqing Liang, Jiancheng Xiao, Wensheng Gan, and Philip S. Yu delve into the rapidly advancing field of artificial intelligence technology and its extensive application in large language models (LLMs). These LLMs play a crucial role in enhancing production, creativity, learning, and work efficiency across various domains. The potential harm within human society due to misuse of LLMs is addressed by proposing the implementation of LLM watermarking techniques. This approach aims to provide intellectual property protection while enabling traceability of multimedia data generated by these models. The preprint represents a comprehensive review that meticulously investigates and analyzes the intricacies of LLM watermarking technology. It begins by tracing the historical development of traditional watermarking technologies before delving into the current landscape of research on LLM watermarking. By examining existing techniques in this domain, the authors aim to inspire novel applications by integrating traditional digital watermarking methods with advancements in LLM watermarking technology. The advantages and limitations associated with LLM watermarking are critically evaluated in this review. With the evolving trend towards multimodal capabilities in LLMs encompassing visual and audio data, an insightful analysis on emerging multimodal LLM watermarking techniques is provided. This detailed examination serves as a valuable resource for researchers seeking reference points for further exploration. Moreover, the preprint sheds light on challenges faced by current watermarking technologies while offering a glimpse into future prospects within this field. By providing valuable insights and recommendations for future research endeavors and practical applications of LLM watermarking technology, this survey contributes significantly to advancing knowledge at the intersection of artificial intelligence and intellectual property protection.