From Intentions to Techniques: A Comprehensive Taxonomy and Challenges in Text Watermarking for Large Language Models

2024-06-17

North American Chapter of the Association for Computational Linguistics

AI Summary

To address copyright protection needs for large language model (LLM)-generated text, this paper conducts a systematic literature review to establish the first comprehensive taxonomy for LLM text watermarking. Departing from conventional technique-centric taxonomies, we propose an original “intention-driven” three-dimensional framework encompassing design objectives (e.g., verifiability, robustness), dataset characteristics, and embedding/removal methodologies—while uniquely integrating evaluation benchmarks, application scenarios, and technical limitations. Our analysis uncovers critical gaps in dynamic content adaptation, cross-model generalizability, preservation of human readability, and standardized evaluation. The taxonomy unifies fragmented research efforts and provides a coherent theoretical foundation alongside an extensible research roadmap, thereby advancing foundational support for authorship rights protection in generative text.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

With the rapid growth of Large Language Models (LLMs), safeguarding textual content against unauthorized use is crucial. Text watermarking offers a vital solution, protecting both - LLM-generated and plain text sources. This paper presents a unified overview of different perspectives behind designing watermarking techniques, through a comprehensive survey of the research literature. Our work has two key advantages, (1) we analyze research based on the specific intentions behind different watermarking techniques, evaluation datasets used, watermarking addition, and removal methods to construct a cohesive taxonomy. (2) We highlight the gaps and open challenges in text watermarking to promote research in protecting text authorship. This extensive coverage and detailed analysis sets our work apart, offering valuable insights into the evolving landscape of text watermarking in language models.

Problem

• Classify text watermarking techniques by intentions and methods
• Identify gaps and challenges in text watermarking research
• Protect authorship of LLM-generated and plain text content

Innovation

• Comprehensive taxonomy of text watermarking techniques
• Analysis based on intentions, datasets, and methods
• Highlights gaps and challenges in text watermarking