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A Survey of Text Watermarking in the Era of Large Language Models

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arxiv 2312.07913 v6 pith:FC3MPRHH submitted 2023-12-13 cs.CL

classification cs.CL
keywords textwatermarkingalgorithmssurveytechnologyapplicationcurrentlanguage
verification ladder T0 review T1 audit T2 compute T3 formal
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Text watermarking algorithms are crucial for protecting the copyright of textual content. Historically, their capabilities and application scenarios were limited. However, recent advancements in large language models (LLMs) have revolutionized these techniques. LLMs not only enhance text watermarking algorithms with their advanced abilities but also create a need for employing these algorithms to protect their own copyrights or prevent potential misuse. This paper conducts a comprehensive survey of the current state of text watermarking technology, covering four main aspects: (1) an overview and comparison of different text watermarking techniques; (2) evaluation methods for text watermarking algorithms, including their detectability, impact on text or LLM quality, robustness under target or untargeted attacks; (3) potential application scenarios for text watermarking technology; (4) current challenges and future directions for text watermarking. This survey aims to provide researchers with a thorough understanding of text watermarking technology in the era of LLM, thereby promoting its further advancement.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 4 citations worldwide. Full citation record

  1. Marking the Wrong Symptoms: Evaluating LLM Watermarks in Medical Texts

    cs.AI 2026-05 conditional novelty 6.0 of 10

    Watermarking medical AI outputs can degrade reasoning, terminology, and image interpretation even when benchmark accuracy stays stable, so accuracy-only evaluations hide clinically important damage.

  2. Optimizing Token Choice for Code Watermarking: An RL Approach

    cs.CR 2025-08 unverdicted novelty 6.0 of 10

    An RL-trained policy adaptively biases token choices to watermark LLM-generated code while preserving executable behavior.

  3. Private, Verifiable, and Auditable AI Systems

    cs.CR 2025-08 conditional novelty 4.0 of 10

    A thesis demonstrating partial prototypes for zk-verifiable model evaluation and privacy-preserving retrieval, and arguing these pieces can compose into end-to-end auditable AI systems.

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