Leyi Pan, Aiwei Liu, Zhiwei He, Zitian Gao, Xuandong Zhao, Yijian Lu, Binglin Zhou, Shuliang Liu, Xuming Hu, Lijie Wen, Irwin King, Philip Yu
| Challenge: | Large Language Models (LLMs) embed imperceptible yet algorithmically detectable signals in outputs to identify LLM-generated text. |
| Approach: | They propose to develop an open-source toolkit for LLM watermarking that embeds imperceptible yet algorithmically detectable signals in model outputs to identify LLM-generated text. |
| Outcome: | MarkLLM provides a unified framework for implementing LLM watermarking algorithms, while providing user-friendly interfaces to ensure ease of access. |
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WaterBench: Towards Holistic Evaluation of Watermarks for Large Language Models (2024.acl-long)
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| Challenge: | Recent studies have developed watermarking algorithms which restrict the generation process to leave an invisible trace for watermark detection. |
| Approach: | They propose a benchmarking procedure that compares different methods to ensure consistent watermarking strength and jointly evaluates their generation and detection performance. |
| Outcome: | The proposed benchmark compares 4 open-source watermarks on 2 LLMs under 2 watermarking strengths and observes the common struggles for current methods on maintaining the generation quality. |
PostMark: A Robust Blackbox Watermark for Large Language Models (2024.emnlp-main)
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| Challenge: | Existing methods to detect LLM-generated text require access to the underlying LLM’s logits, which LLM providers are loath to share due to fears of model distillation. |
| Approach: | They develop a post-hoc watermarking procedure that inserts an input-dependent set of words into the text after the decoding process has completed. |
| Outcome: | The proposed method is more robust to paraphrasing attacks than existing methods. |
Watermark under Fire: A Robustness Evaluation of LLM Watermarking (2025.findings-emnlp)
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| Challenge: | Various watermarking methods have been proposed to identify LLM-generated texts . lack of unified evaluation platforms has left many critical questions unanswered . |
| Approach: | They systematize existing LLM watermarkers and watermark removal attacks and develop a unified platform that integrates them. |
| Outcome: | The proposed systematizes existing LLM watermarkers and watermark removal attacks, mapping out their design spaces. |
From Intentions to Techniques: A Comprehensive Taxonomy and Challenges in Text Watermarking for Large Language Models (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) are rapidly growing and allowing textual content to be protected against unauthorized use. |
| Approach: | They present a unified overview of different perspectives behind designing watermarking techniques through a comprehensive survey of the research literature. |
| Outcome: | The proposed methods are based on the evaluation datasets used and watermarking addition and removal methods to construct a taxonomy. |
WaterJudge: Quality-Detection Trade-off when Watermarking Large Language Models (2024.findings-naacl)
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| Challenge: | Recent work has shown that small, context-dependent shifts in word distributions can be used to apply and detect watermarks, but little work has analyzed the impact of these perturbations on the quality of generated texts. |
| Approach: | They propose a framework that allows for analysis of the impact of watermark settings on the quality of generated texts. |
| Outcome: | The proposed framework provides easy visualization of the quality-detection trade-off of watermark settings. |
Marking Code Without Breaking It: Code Watermarking for Detecting LLM-Generated Code (2026.findings-eacl)
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| Challenge: | Existing methods for identifying LLM-generated code are limited by syntax-critical tokens, which can introduce syntax errors. |
| Approach: | They propose a syntax-aware watermarking method that embeds watermarks only in non-syntactic tokens and preserves code integrity. |
| Outcome: | The proposed method outperforms baseline methods on Python, C++, and Java. |
Position: LLM Watermarking Should Align Stakeholders’ Incentives for Practical Adoption (2026.findings-acl)
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| Challenge: | despite advances in watermarking algorithms, real-world deployment remains limited . model watermarks can be used to protect intellectual property and promote trust in AI . |
| Approach: | They propose to revisit three classes of watermarking to examine incentives for large language models . model watermarks naturally aligns with LLM provider interests, yet faces new challenges in open-source ecosystems . |
| Outcome: | The proposed methods can be used in dataset decontamination, user-controlled provenance, and in-context watermarking. |
Where Am I From? Identifying Origin of LLM-generated Content (2024.emnlp-main)
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| Challenge: | Generative models have produced high-quality content, but they pose security risks . a new framework for deep learning systems enables the tracing of AI-generated content back to its source . |
| Approach: | They propose a digital forensics framework that embeds a secret watermark into the generated output and a "depth watermark" this watermark strengthens the link between content and generator, enabling accurate tracing while maintaining the quality of the generated content. |
| Outcome: | The proposed framework ensures accurate tracing while maintaining quality of generated content. |
WASA: WAtermark-based Source Attribution for Large Language Model-Generated Data (2025.findings-acl)
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Xinyang Lu, Jingtan Wang, Zitong Zhao, Zhongxiang Dai, Chuan-Sheng Foo, See-Kiong Ng, Bryan Kian Hsiang Low
| Challenge: | Large language models (LLMs) have impressive performance but intellectual property concerns are looming . a framework that can be used to perform source attribution for LLMs can be developed. |
| Approach: | They propose a framework that enables an LLM to generate synthetic texts with embedded watermarks that contain information about their source. |
| Outcome: | The proposed framework achieves source attribution accuracy and robustness against adversaries. |
SpARK: An Embarrassingly Simple Sparse Watermarking in LLMs with Enhanced Text Quality (2026.findings-eacl)
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| Challenge: | Existing methods for detecting and monitoring generated text face a trade-off between the quality of the generated text and the effectiveness of the watermarking process. |
| Approach: | They propose a new type of LLM watermark, Sparse WatermARK, which uses watermarks to a small subset of generated tokens distributed across the text. |
| Outcome: | The proposed method outperforms existing methods in detectability and quality while maintaining generated text quality. |