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TripPattern introduces a novel text watermarking framework that preserves LLM generation quality by using pattern-based token alternation rather than restricting vocabulary choices. This approach offers interpretable statistical detection for identifying machine-generated content while mitigating the naturalness degradation common in existing methods. The research advances practical AI safety mechanisms essential for distinguishing synthetic text from human writing in multilingual contexts.
TripPattern: A Pattern-based Text Watermarking Method for Large Language Models
arXiv:2609.12472v1 Announce Type: new 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. TripPattern divides the vocabulary into one neutral group and two pattern groups. During generation, the model alternates token selection between the two pattern groups to embed detectable patterns, while neutral tokens are selected independently to improve flexibility and preserve naturalness. For detection, TripPattern uses pattern-based statistical tests that provide interpretable p-values by measuring how often adjacent tokens alternate between the pattern groups. Theoretical analysis and empirical evaluations on four multilingual datasets show that TripPattern maintains LLM generation quality while achieving robust watermark detectability.
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. TripPattern divides the vocabulary into one neutral group and two pattern groups. During generation, the model alternates token selection between the two pattern groups to embed detectable patterns, while neutral tokens are selected independently to improve flexibility and preserve naturalness. For detection, TripPattern uses pattern-based statistical tests that provide interpretable p-values by measuring how often adjacent tokens alternate between the pattern groups. Theoretical analysis and empirical evaluations on four multilingual datasets show that TripPattern maintains LLM generation quality while achieving robust watermark detectability.
Accepted to Findings of AACL-IJCNLP 2026. 16 pages, 4 figures, 10 tables
Artificial Intelligence (cs.AI)
arXiv:2609.12472 [cs.AI]
(or arXiv:2609.12472v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.12472 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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