给大模型和生成内容加水印，防抄袭与造假。
Watermarking Large Language Models and the Generated Content: Opportunities and Challenges
- 在不同威胁场景下为大模型本身加水印。
- 设计可抵抗攻击的生成内容水印方法。
- 适合关注AI版权与安全的研究者阅读。
广泛使用且强大的生成式大语言模型（LLMs）引发了知识产权侵权和机器生成虚假信息传播的担忧。水印技术有望确立所有权、防止未经授权使用并追踪生成内容的来源。本文总结了在对大模型及其生成内容进行水印时遇到的挑战与机遇。首先介绍了在不同威胁模型和场景下对大模型本身实施水印的技术。接着研究了针对大模型生成内容设计的水印方法，评估其在各种攻击下的有效性和鲁棒性。还强调了对代码生成、芯片设计和医疗应用等特定领域模型与数据进行水印的重要性。此外，探讨了硬件加速等方法以提升水印处理效率。最后，讨论了当前方法的局限性，并提出了未来负责任保护生成式AI工具的研究方向。
原文摘要 · Abstract (English)
The widely adopted and powerful generative large language models (LLMs) have raised concerns about intellectual property rights violations and the spread of machine-generated misinformation. Watermarking serves as a promising approch to establish ownership, prevent unauthorized use, and trace the origins of LLM-generated content. This paper summarizes and shares the challenges and opportunities we found when watermarking LLMs. We begin by introducing techniques for watermarking LLMs themselves under different threat models and scenarios. Next, we investigate watermarking methods designed for the content generated by LLMs, assessing their effectiveness and resilience against various attacks. We also highlight the importance of watermarking domain-specific models and data, such as those used in code generation, chip design, and medical applications. Furthermore, we explore methods like hardware acceleration to improve the efficiency of the watermarking process. Finally, we discuss the limitations of current approaches and outline future research directions for the responsible use and protection of these generative AI tools.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。