# REMARK-LLM: A Robust and Efficient Watermarking Framework for Generative Large Language Models

arXiv: https://arxiv.org/abs/2310.12362 · HTML: https://mlsanity.com/paper/2310.12362/

## Key facts

- Cited by 78 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 26 of its 48 references that are in the map.
- First posted on arXiv 18 Oct 2023.

## Placed in

- LLM safety and security / [Watermarking generated content and models](https://mlsanity.com/safety/watermarking-generated-content-and-models/)

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Pointer Sentinel Mixture Models](https://mlsanity.com/paper/1609.07843/) (2016)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [A Watermark for Large Language Models](https://mlsanity.com/paper/2301.10226/) (2023)
- [How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection](https://mlsanity.com/paper/2301.07597/) (2023)
- [Robust Distortion-free Watermarks for Language Models](https://mlsanity.com/paper/2307.15593/) (2023)
- [Provable Robust Watermarking for AI-Generated Text](https://mlsanity.com/paper/2306.17439/) (2023)
- [Undetectable Watermarks for Language Models](https://mlsanity.com/paper/2306.09194/) (2023)
- [On the Reliability of Watermarks for Large Language Models](https://mlsanity.com/paper/2306.04634/) (2023)
- [Can Foundation Models Wrangle Your Data?](https://mlsanity.com/paper/2205.09911/) (2022)
- [Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hiding](https://mlsanity.com/paper/2009.03015/) (2020)
- [The Science of Detecting LLM-Generated Texts](https://mlsanity.com/paper/2303.07205/) (2023)
- [Protecting Language Generation Models via Invisible Watermarking](https://mlsanity.com/paper/2302.03162/) (2023)
- [Protecting Intellectual Property of Language Generation APIs with Lexical Watermark](https://mlsanity.com/paper/2112.02701/) (2021)
- [Robust Multi-bit Natural Language Watermarking through Invariant Features](https://mlsanity.com/paper/2305.01904/) (2023)
- [CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks](https://mlsanity.com/paper/2209.08773/) (2022)
- [Watermarking Text Generated by Black-Box Language Models](https://mlsanity.com/paper/2305.08883/) (2023)
- [Deep Model Intellectual Property Protection via Deep Watermarking](https://mlsanity.com/paper/2103.04980/) (2021)
- [FaceSigns: Semi-Fragile Neural Watermarks for Media Authentication and Countering Deepfakes](https://mlsanity.com/paper/2204.01960/) (2022)

## Built on by (most cited first, 30 of 78)

- [A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2312.02003/) (2023)
- [A Survey of Text Watermarking in the Era of Large Language Models](https://mlsanity.com/paper/2312.07913/) (2023)
- [On-Device Language Models: A Comprehensive Review](https://mlsanity.com/paper/2409.00088/) (2024)
- [Adaptive Text Watermark for Large Language Models](https://mlsanity.com/paper/2401.13927/) (2024)
- [Provably Robust Multi-bit Watermarking for AI-generated Text](https://mlsanity.com/paper/2401.16820/) (2024)
- [Token-Specific Watermarking with Enhanced Detectability and Semantic Coherence for Large Language Models](https://mlsanity.com/paper/2402.18059/) (2024)
- [Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2502.06872/) (2025)
- [Detecting AI-Generated Text: Factors Influencing Detectability with Current Methods](https://mlsanity.com/paper/2406.15583/) (2024)
- [Theoretically Grounded Framework for LLM Watermarking: A Distribution-Adaptive Approach](https://mlsanity.com/paper/2410.02890/) (2024)
- [Double-I Watermark: Protecting Model Copyright for LLM Fine-tuning](https://mlsanity.com/paper/2402.14883/) (2024)
- [Frontier AI's Impact on the Cybersecurity Landscape](https://mlsanity.com/paper/2504.05408/) (2025)
- [BiMark: Unbiased Multilayer Watermarking for Large Language Models](https://mlsanity.com/paper/2506.21602/) (2025)
- [Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends](https://mlsanity.com/paper/2508.11548/) (2025)
- [Watermarking Autoregressive Image Generation](https://mlsanity.com/paper/2506.16349/) (2025)
- [Waterfall: Framework for Robust and Scalable Text Watermarking and Provenance for LLMs](https://mlsanity.com/paper/2407.04411/) (2024)
- [SoK: Large Language Model Copyright Auditing via Fingerprinting](https://mlsanity.com/paper/2508.19843/) (2025)
- [A Watermark for Order-Agnostic Language Models](https://mlsanity.com/paper/2410.13805/) (2024)
- [Can Watermarked LLMs be Identified by Users via Crafted Prompts?](https://mlsanity.com/paper/2410.03168/) (2024)
- [Survey on AI-Generated Media Detection: From Non-MLLM to MLLM](https://mlsanity.com/paper/2502.05240/) (2025)
- [StealthInk: A Multi-bit and Stealthy Watermark for Large Language Models](https://mlsanity.com/paper/2506.05502/) (2025)
- [Robust Multi-bit Text Watermark with LLM-based Paraphrasers](https://mlsanity.com/paper/2412.03123/) (2024)
- [Defending LLM Watermarking Against Spoofing Attacks with Contrastive Representation Learning](https://mlsanity.com/paper/2504.06575/) (2025)
- [In-Context Watermarks for Large Language Models](https://mlsanity.com/paper/2505.16934/) (2025)
- [Topic-Based Watermarks for Large Language Models](https://mlsanity.com/paper/2404.02138/) (2024)
- [GeneBreaker: Jailbreak Attacks against DNA Language Models with Pathogenicity Guidance](https://mlsanity.com/paper/2505.23839/) (2025)
- [DATABench: Evaluating Dataset Auditing in Deep Learning from an Adversarial Perspective](https://mlsanity.com/paper/2507.05622/) (2025)
- [On Protecting Agentic Systems' Intellectual Property via Watermarking](https://mlsanity.com/paper/2602.08401/) (2026)
- [PersonaMark: Personalized LLM watermarking for model protection and user attribution](https://mlsanity.com/paper/2409.09739/) (2024)
- [SAEMark: Steering Personalized Multilingual LLM Watermarks with Sparse Autoencoders](https://mlsanity.com/paper/2508.08211/) (2025)
- [Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoption](https://mlsanity.com/paper/2510.18333/) (2025)

## Abstract (arXiv)

We present REMARK-LLM, a novel efficient, and robust watermarking framework designed for texts generated by large language models (LLMs). Synthesizing human-like content using LLMs necessitates vast computational resources and extensive datasets, encapsulating critical intellectual property (IP). However, the generated content is prone to malicious exploitation, including spamming and plagiarism. To address the challenges, REMARK-LLM proposes three new components: (i) a learning-based message encoding module to infuse binary signatures into LLM-generated texts; (ii) a reparameterization module to transform the dense distributions from the message encoding to the sparse distribution of the watermarked textual tokens; (iii) a decoding module dedicated for signature extraction; Furthermore, we introduce an optimized beam search algorithm to guarantee the coherence and consistency of the generated content. REMARK-LLM is rigorously trained to encourage the preservation of semantic integrity in watermarked content, while ensuring effective watermark retrieval. Extensive evaluations on multiple unseen datasets highlight REMARK-LLM proficiency and transferability in inserting 2 times more signature bits into the same texts when compared to prior art, all while maintaining semantic integrity. Furthermore, REMARK-LLM exhibits better resilience against a spectrum of watermark detection and removal attacks.