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Watermarking Large Language Models and the Generated Content: Opportunities and Challenges

Ruisi Zhang, Farinaz Koushanfar

TL;DR

This paper summarizes and shares the challenges and opportunities found when watermarking generative large language models, and highlights the importance of watermarking domain-specific models and data, such as those used in code generation, chip design, and medical applications.

Abstract

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.

Watermarking Large Language Models and the Generated Content: Opportunities and Challenges

TL;DR

This paper summarizes and shares the challenges and opportunities found when watermarking generative large language models, and highlights the importance of watermarking domain-specific models and data, such as those used in code generation, chip design, and medical applications.

Abstract

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.

Paper Structure

This paper contains 18 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Watermarking Large Language Models
  3. Watermarking Open-source Models
  4. Watermarking Embedded Models
  5. Potential Threats
  6. Watermarking LLM-Generated Contents
  7. Natural Language Data Watermarking
  8. Domain-Specific Data Watermarking
  9. Potential Threats
  10. Watermarking Acceleration
  11. Model Watermarking Acceleration
  12. Content Watermarking Acceleration
  13. Future Direction
  14. Robustness against Evolving Attacks
  15. Adaptive Regulation and Authentication
  16. ...and 3 more sections

Figures (3)

  • Figure 1: Paper overview. The paper first introduces LLM model watermarking and LLM-generated content watermarking. Then, it presents methods for watermark insertion and verification acceleration.
  • Figure 2: Embeded LLM watermarking overview. The watermark insertion encodes signatures into the original LLM before deployment. The watermark extraction decodes the signatures from the deployed LLM and proves ownership.
  • Figure 3: LLM-generated text watermarking scenario. The local user sends prompts to the remote LLM cloud API, and the API watermarks (WM) the responded texts before sending them back to users. LLM proprietor claims ownership by using the message decoding module to decode the signatures and compare them with inserted watermarks.