mlsanityData to 14 Oct 2026

From Intentions to Techniques: A Comprehensive Taxonomy and Challenges in Text Watermarking for Large Language Models

arXiv 2406.11106 ↗First posted 17 Jun 2024cs.CL cs.AIcited by 3

On the map: LLM safety and security → Watermarking generated content and models

Abstract (arXiv)

With the rapid growth of Large Language Models (LLMs), safeguarding textual content against unauthorized use is crucial. Watermarking offers a vital solution, protecting both - LLM-generated and plain text sources. This paper presents a unified overview of different perspectives behind designing watermarking techniques through a comprehensive survey of the research literature. Our work has two key advantages: (1) We analyze research based on the specific intentions behind different watermarking techniques, evaluation datasets used, and watermarking addition and removal methods to construct a cohesive taxonomy. (2) We highlight the gaps and open challenges in text watermarking to promote research protecting text authorship. This extensive coverage and detailed analysis sets our work apart, outlining the evolving landscape of text watermarking in Language Models.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security. Builds on 38 of its 83 references that are in the map. First posted on arXiv 17 Jun 2024.

Builds on

38 of 83 references.

Closest ancestors · 17

Same direction, newest first

  1. 2024Bileve: Securing Text Provenance in Large Language Models Against Spoofing with Bi-level Signature4 Jun
  2. 2024Explanation as a Watermark: Towards Harmless and Multi-bit Model Ownership Verification via Watermarking Feature Attribution8 May
  3. 2024CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code24 Apr
  4. 2023A Robust Semantics-based Watermark for Large Language Model against Paraphrasing15 Nov
  5. 2023Publicly-Detectable Watermarking for Language Models27 Oct
  6. 2023CodeMark: Imperceptible Watermarking for Code Datasets against Neural Code Completion Models28 Aug
  7. 2023Robust Distortion-free Watermarks for Language Models28 Jul
  8. 2023Watermarking Conditional Text Generation for AI Detection: Unveiling Challenges and a Semantic-Aware Watermark Remedy25 Jul
  9. 2023Provable Robust Watermarking for AI-Generated Text30 Jun
  10. 2023Who Wrote this Code? Watermarking for Code Generation24 May
  11. 2023Watermarking Text Data on Large Language Models for Dataset Copyright22 May
  12. 2023Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermark17 May
5 more
  1. 2023Watermarking Text Generated by Black-Box Language Models14 May
  2. 2023Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense23 Mar
  3. 2023Protecting Language Generation Models via Invisible Watermarking6 Feb
  4. 2022CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks19 Sep
  5. 2021Protecting Intellectual Property of Language Generation APIs with Lexical Watermark5 Dec

Other references · 21

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  4. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  5. 2016Pointer Sentinel Mixture Modelscited by 3,096
  6. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,975
  7. 2015Character-level Convolutional Networks for Text Classificationcited by 2,330
  8. 2018Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarizationcited by 1,317
  9. 2021Measuring Coding Challenge Competence With APPScited by 902
  10. 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 812
  11. 2019CodeSearchNet Challenge: Evaluating the State of Semantic Code Searchcited by 654
  12. 2023How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detectioncited by 481
9 more
  1. 2022DS-1000: A Natural and Reliable Benchmark for Data Science Code Generationcited by 452
  2. 2022Multi-lingual Evaluation of Code Generation Modelscited by 159
  3. 2020DART: Open-Domain Structured Data Record to Text Generationcited by 145
  4. 2020Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hidingcited by 130
  5. 2018A standardized Project Gutenberg corpus for statistical analysis of natural language and quantitative linguisticscited by 64
  6. 2023Paloma: A Benchmark for Evaluating Language Model Fitcited by 54
  7. 2024The Ethics of Interaction: Mitigating Security Threats in LLMscited by 33
  8. 2023Towards Code Watermarking with Dual-Channel Transformationscited by 9
  9. 2023Towards Tracing Code Provenance with Code Watermarkingcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2024Watermarking Techniques for Large Language Models: A Surveycited by 23
  2. 2025SoK: Are Watermarks in LLMs Ready for Deployment?cited by 2
  3. 2026LLM Watermarking as Big Data Provenance: A Deployment-Oriented Systematizationcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2023A Survey of Text Watermarking in the Era of Large Language Models
  2. 2023Necessary and Sufficient Watermark for Large Language Models
  3. 2025DERMARK: A Dynamic, Efficient and Robust Multi-bit Watermark for Large Language Models
  4. 2025Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends
  5. 2025PRO: Enabling Precise and Robust Text Watermark for Open-Source LLMs
  6. 2023Mark My Words: Analyzing and Evaluating Language Model Watermarks
  7. 2024Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data?
  8. 2023Baselines for Identifying Watermarked Large Language Models