With the rapid growth of Large Language Models (LLMs), safeguarding textual content against unauthorized use is crucial. 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, and watermarking addition and removal methods to construct a cohesive taxonomy. (2) We highlight the gaps and open challenges in text watermarking to promote research protecting text authorship. This extensive coverage and detailed analysis sets our work apart, outlining the evolving landscape of text watermarking in Language Models.
@article{arxiv.2406.11106,
title = {From Intentions to Techniques: A Comprehensive Taxonomy and Challenges in Text Watermarking for Large Language Models},
author = {Harsh Nishant Lalai and Aashish Anantha Ramakrishnan and Raj Sanjay Shah and Dongwon Lee},
journal= {arXiv preprint arXiv:2406.11106},
year = {2025}
}