paperSeptember 2026Unreviewed

TripPattern: A Pattern-based Text Watermarking Method for Large Language Models

Sang-Jun Moon, Dasom Choi, Jingun Kwon, Hidetaka Kamigaito, Taro Watanabe, Manabu Okumura

Abstract

Text watermarking techniques have gained significant attention for identifying machine-generated text and mitigating risks from large language models (LLMs). Existing methods typically divide an LLM's vocabulary into green and red tokens, but encouraging generation toward green tokens can reduce text quality and naturalness. To address this, we propose TripPattern, a watermarking framework that formulates text watermarking as a pattern-based matching task using three vocabulary partitions. TripP

Cite

@misc{moon2026trippattern,
  title = {{TripPattern: A Pattern-based Text Watermarking Method for Large Language Models}},
  author = {Sang-Jun Moon and Dasom Choi and Jingun Kwon and Hidetaka Kamigaito and Taro Watanabe and Manabu Okumura},
  year = {2026},
  month = sep,
  eprint = {2609.12472},
  archivePrefix = {arXiv},
  url = {https://www.semanticscholar.org/paper/d7035dd21e2547207c23fec06a3b22bf29586847}
}