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Embarrassingly Simple Text Watermarks

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arxiv 2310.08920 v1 pith:3EVX2OW2 submitted 2023-10-13 cs.LG cs.AIcs.CR

classification cs.LGcs.AIcs.CR
keywords easymarktextsimplellmstextswatermarkwatermarkingwatermarks
verification ladder T0 review T1 audit T2 compute T3 formal
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We propose Easymark, a family of embarrassingly simple yet effective watermarks. Text watermarking is becoming increasingly important with the advent of Large Language Models (LLM). LLMs can generate texts that cannot be distinguished from human-written texts. This is a serious problem for the credibility of the text. Easymark is a simple yet effective solution to this problem. Easymark can inject a watermark without changing the meaning of the text at all while a validator can detect if a text was generated from a system that adopted Easymark or not with high credibility. Easymark is extremely easy to implement so that it only requires a few lines of code. Easymark does not require access to LLMs, so it can be implemented on the user-side when the LLM providers do not offer watermarked LLMs. In spite of its simplicity, it achieves higher detection accuracy and BLEU scores than the state-of-the-art text watermarking methods. We also prove the impossibility theorem of perfect watermarking, which is valuable in its own right. This theorem shows that no matter how sophisticated a watermark is, a malicious user could remove it from the text, which motivate us to use a simple watermark such as Easymark. We carry out experiments with LLM-generated texts and confirm that Easymark can be detected reliably without any degradation of BLEU and perplexity, and outperform state-of-the-art watermarks in terms of both quality and reliability.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Toward Stronger Code Watermarking: A Grammar-Driven Approach to Optimizing the Trade-off Between Quality and Detectability

    cs.CR 2026-07 conditional novelty 5.5 of 10

    Grammar-guided three-level masking plus role-aware logit bias and weighted detection improves the code quality–watermark detectability frontier over KGW, SWEET, EWD, STONE, CodeIP, and SynthID-Text.

  2. Invisible Entropy: Towards Safe and Efficient Low-Entropy LLM Watermarking

    cs.CL 2025-05 conditional novelty 5.0 of 10

    A lightweight entropy classifier plus an adaptive threshold method can watermark and detect low-entropy LLM code outputs without querying the original model, matching much larger detectors at 99% fewer detection-phase...

  3. Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey

    cs.CL 2025-02 conditional novelty 4.0 of 10

    A survey organizes current research on trustworthy RAG into six pillars, reliability, privacy, safety, fairness, explainability, and accountability, and maps methods, metrics, and open problems for each.

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