大型语言模型水印技术：综述
Watermarking Techniques for Large Language Models: A Survey
AI总结：
本文系统综述了大型语言模型水印技术，梳理其与传统水印的继承关系，分析优缺点及多模态水印发展，并展望挑战与未来方向。
摘要：
随着人工智能技术的快速进步和广泛应用，大型语言模型（LLMs）被广泛用于提升各领域的生产、创造力、学习与工作效率。然而，LLMs的滥用也给人类社会带来潜在危害，例如知识产权问题、学术不端、虚假内容和幻觉。相关研究提出利用LLM水印来实现LLM的知识产权保护以及LLM输出多媒体数据的可追溯性。据我们所知，这是首篇对LLM水印技术进行详细调查与分析的全面综述。本综述首先回顾传统水印技术的历史，然后分析LLM水印研究的现状，并深入考察这些技术之间的继承性与关联性。通过分析其继承性与关联性，本综述可为将传统数字水印技术应用于LLM水印的研究提供思路，从而促进水印技术的交叉融合与创新。此外，本综述还考察了LLM水印的优缺点。考虑到当前LLM的多模态发展趋势，本综述对新兴的多模态LLM水印（如视觉和音频数据）进行了详细分析，以期为相关研究提供更多参考思路。本综述深入探讨了当前水印技术面临的挑战与未来前景，为未来LLM水印研究与应用提供了有价值的见解。
英文摘要：
With the rapid advancement and extensive application of artificial intelligence technology, large language models (LLMs) are extensively used to enhance production, creativity, learning, and work efficiency across various domains. However, the abuse of LLMs also poses potential harm to human society, such as intellectual property rights issues, academic misconduct, false content, and hallucinations. Relevant research has proposed the use of LLM watermarking to achieve IP protection for LLMs and traceability of multimedia data output by LLMs. To our knowledge, this is the first thorough review that investigates and analyzes LLM watermarking technology in detail. This review begins by recounting the history of traditional watermarking technology, then analyzes the current state of LLM watermarking research, and thoroughly examines the inheritance and relevance of these techniques. By analyzing their inheritance and relevance, this review can provide research with ideas for applying traditional digital watermarking techniques to LLM watermarking, to promote the cross-integration and innovation of watermarking technology. In addition, this review examines the pros and cons of LLM watermarking. Considering the current multimodal development trend of LLMs, it provides a detailed analysis of emerging multimodal LLM watermarking, such as visual and audio data, to offer more reference ideas for relevant research. This review delves into the challenges and future prospects of current watermarking technologies, offering valuable insights for future LLM watermarking research and applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
