# MorphMark: Flexible Adaptive Watermarking for Large Language Models

arXiv: https://arxiv.org/abs/2505.11541 · HTML: https://mlsanity.com/paper/2505.11541/

## Key facts

- Cited by 24 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 35 of its 48 references that are in the map.
- First posted on arXiv 14 May 2025.

## 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, 30 of 35)

- [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)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [VBench: Comprehensive Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2311.17982/) (2023)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators](https://mlsanity.com/paper/2404.04475/) (2024)
- [StarCoder 2 and The Stack v2: The Next Generation](https://mlsanity.com/paper/2402.19173/) (2024)
- [From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline](https://mlsanity.com/paper/2406.11939/) (2024)
- [Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense](https://mlsanity.com/paper/2303.13408/) (2023)
- [Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring](https://mlsanity.com/paper/1802.04633/) (2018)
- [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)
- [Who Wrote this Code? Watermarking for Code Generation](https://mlsanity.com/paper/2305.15060/) (2023)
- [ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation](https://mlsanity.com/paper/2406.09961/) (2024)
- [A Thorough Examination of Decoding Methods in the Era of LLMs](https://mlsanity.com/paper/2402.06925/) (2024)
- [MarkLLM: An Open-Source Toolkit for LLM Watermarking](https://mlsanity.com/paper/2405.10051/) (2024)
- [Watermark Stealing in Large Language Models](https://mlsanity.com/paper/2402.19361/) (2024)
- [A Robust Semantics-based Watermark for Large Language Model against Paraphrasing](https://mlsanity.com/paper/2311.08721/) (2023)
- [An Entropy-based Text Watermarking Detection Method](https://mlsanity.com/paper/2403.13485/) (2024)
- [InsCL: A Data-efficient Continual Learning Paradigm for Fine-tuning Large Language Models with Instructions](https://mlsanity.com/paper/2403.11435/) (2024)
- [WaterMax: breaking the LLM watermark detectability-robustness-quality trade-off](https://mlsanity.com/paper/2403.04808/) (2024)
- [No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices](https://mlsanity.com/paper/2402.16187/) (2024)

## Built on by (most cited first, 24 of 24)

- [Hume: Introducing System-2 Thinking in Visual-Language-Action Model](https://mlsanity.com/paper/2505.21432/) (2025)
- [PMark: Towards Robust and Distortion-free Semantic-level Watermarking with Channel Constraints](https://mlsanity.com/paper/2509.21057/) (2025)
- [Large Language Model Sourcing: A Survey](https://mlsanity.com/paper/2510.10161/) (2025)
- [TextSeal: A Localized LLM Watermark for Provenance & Distillation Protection](https://mlsanity.com/paper/2605.12456/) (2026)
- [Majority Bit-Aware Watermarking For Large Language Models](https://mlsanity.com/paper/2508.03829/) (2025)
- [Robustness Assessment and Enhancement of Text Watermarking for Google's SynthID](https://mlsanity.com/paper/2508.20228/) (2025)
- [Distilling the Thought, Watermarking the Answer: A Principle Semantic Guided Watermark for Large Reasoning Models](https://mlsanity.com/paper/2601.05144/) (2026)
- [A Visual Semantic Adaptive Watermark grounded by Prefix-Tuning for Large Vision-Language Model](https://mlsanity.com/paper/2601.07291/) (2026)
- [How Good is Post-Hoc Watermarking With Language Model Rephrasing?](https://mlsanity.com/paper/2512.16904/) (2025)
- [Watermarking Should Be Treated as a Monitoring Primitive](https://mlsanity.com/paper/2605.13095/) (2026)
- [SAMark: A Self-Anchored Text Watermarking with Paragraph-Level Paraphrase Robustness](https://mlsanity.com/paper/2605.25796/) (2026)
- [AGMark: Attention-Guided Dynamic Watermarking for Large Vision-Language Models](https://mlsanity.com/paper/2602.09611/) (2026)
- [LLM Watermarking as Big Data Provenance: A Deployment-Oriented Systematization](https://mlsanity.com/paper/2607.10103/) (2026)
- [WaterMoE: Expert-Routing-based Watermarking for High Fidelity and Efficiency](https://mlsanity.com/paper/2607.13099/) (2026)
- [WaterSearch: Exploring Seed Pooling for Improving the Quality-Detectability Trade-off in LLM Watermarking](https://mlsanity.com/paper/2512.00837/) (2025)
- [AEyeDE: An Attention-Based Attribution Framework for AI-Generated Text Detection](https://mlsanity.com/paper/2606.00016/) (2026)
- [Signature filtering: a lightweight enhancement for statistical watermark detection in large language models](https://mlsanity.com/paper/2606.18430/) (2026)
- [WorldMark: A Plug-and-Play World Knowledge Interface for Cross-Host Language Model Watermarking](https://mlsanity.com/paper/2608.06416/) (2026)
- [Beyond Semantic Narrowing: Robust and Efficient LLM Watermarking with Hamming Neighborhoods](https://mlsanity.com/paper/2609.37218/) (2026)
- [Language Model Fingerprinting Requires Rethinking Watermark Teachers](https://mlsanity.com/paper/2610.04169/) (2026)
- [Invisible Ink, Visible Lies: How Production Watermarking Causes LLMs to Hallucinate](https://mlsanity.com/paper/2610.04860/) (2026)
- [Grammar-Guided Code Watermarking with Green Temperature](https://mlsanity.com/paper/2610.05323/) (2026)
- [Constitution-Guided Watermarking](https://mlsanity.com/paper/2610.09552/) (2026)
- [Pin It on Anyone: Certified Targeted Displacement of Multi-Bit LLM Watermarks Without the Key](https://mlsanity.com/paper/2610.18359/) (2026)

## Abstract (arXiv)

Watermarking by altering token sampling probabilities based on red-green list is a promising method for tracing the origin of text generated by large language models (LLMs). However, existing watermark methods often struggle with a fundamental dilemma: improving watermark effectiveness (the detectability of the watermark) often comes at the cost of reduced text quality. This trade-off limits their practical application. To address this challenge, we first formalize the problem within a multi-objective trade-off analysis framework. Within this framework, we identify a key factor that influences the dilemma. Unlike existing methods, where watermark strength is typically treated as a fixed hyperparameter, our theoretical insights lead to the development of MorphMarka method that adaptively adjusts the watermark strength in response to changes in the identified factor, thereby achieving an effective resolution of the dilemma. In addition, MorphMark also prioritizes flexibility since it is a model-agnostic and model-free watermark method, thereby offering a practical solution for real-world deployment, particularly in light of the rapid evolution of AI models. Extensive experiments demonstrate that MorphMark achieves a superior resolution of the effectiveness-quality dilemma, while also offering greater flexibility and time and space efficiency.