From Intentions to Techniques: A Comprehensive Taxonomy and Challenges in Text Watermarking for Large Language Models
<|reference_start|>From Intentions to Techniques: A Comprehensive Taxonomy and Challenges in Text Watermarking for Large Language Models: 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.<|reference_end|>
@article{lalai2024from,
    title={From Intentions to Techniques: A Comprehensive Taxonomy and Challenges
  in Text Watermarking for Large Language Models},
    author={Harsh Nishant Lalai, Aashish Anantha Ramakrishnan, Raj Sanjay Shah,
  Dongwon Lee},
    journal={arXiv preprint arXiv:2406.11106},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11106},
    primaryClass={cs.CL cs.AI}
}