# GaussMark: A Practical Approach for Structural Watermarking of Language Models

arXiv: https://arxiv.org/abs/2501.13941 · HTML: https://mlsanity.com/paper/2501.13941/

## Key facts

- Cited by 13 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.
- First posted on arXiv 17 Jan 2025.

## Placed in

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

## Built on by (most cited first, 13 of 13)

- [Towards Watermarking of Open-Source LLMs](https://mlsanity.com/paper/2502.10525/) (2025)
- [Watermarking Diffusion Language Models](https://mlsanity.com/paper/2509.24368/) (2025)
- [LLM Fingerprinting via Semantically Conditioned Watermarks](https://mlsanity.com/paper/2505.16723/) (2025)
- [Diversity Boosts AI-Generated Text Detection](https://mlsanity.com/paper/2509.18880/) (2025)
- [A Reinforcement Learning Framework for Robust and Secure LLM Watermarking](https://mlsanity.com/paper/2510.21053/) (2025)
- [PRO: Enabling Precise and Robust Text Watermark for Open-Source LLMs](https://mlsanity.com/paper/2510.23891/) (2025)
- [More Human or More AI? Visualizing Human-AI Collaboration Disclosures in Journalistic News Production](https://mlsanity.com/paper/2601.11072/) (2026)
- [LLM Watermarking Using Mixtures and Statistical-to-Computational Gaps](https://mlsanity.com/paper/2505.01484/) (2025)
- [Watermarking Large Language Models in Europe: Interpreting the AI Act in Light of Technology](https://mlsanity.com/paper/2511.03641/) (2025)
- [MarkTune: Improving the Quality-Detectability Trade-off in Open-Weight LLM Watermarking](https://mlsanity.com/paper/2512.04044/) (2025)
- [Making Open-Source Text LLM Watermarks Durable Against Merging](https://mlsanity.com/paper/2607.20435/) (2026)
- [OpenStamp: A Watermark for Open-Source Language Models](https://mlsanity.com/paper/2608.27899/) (2026)
- [WoE Wrote It? Watermarking Mixture-of-Experts LLMs for Black-Box Text Provenance](https://mlsanity.com/paper/2608.29151/) (2026)

## Abstract (arXiv)

Recent advances in Large Language Models (LLMs) have led to significant improvements in natural language processing tasks, but their ability to generate human-quality text raises significant ethical and operational concerns in settings where it is important to recognize whether or not a given text was generated by a human. Thus, recent work has focused on developing techniques for watermarking LLM-generated text, i.e., introducing an almost imperceptible signal that allows a provider equipped with a secret key to determine if given text was generated by their model. Current watermarking techniques are often not practical due to concerns with generation latency, detection time, degradation in text quality, or robustness. Many of these drawbacks come from the focus on token-level watermarking, which ignores the inherent structure of text. In this work, we introduce a new scheme, GaussMark, that is simple and efficient to implement, has formal statistical guarantees on its efficacy, comes at no cost in generation latency, and embeds the watermark into the weights of the model itself, providing a structural watermark. Our approach is based on Gaussian independence testing and is motivated by recent empirical observations that minor additive corruptions to LLM weights can result in models of identical (or even improved) quality. We show that by adding a small amount of Gaussian noise to the weights of a given LLM, we can watermark the model in a way that is statistically detectable by a provider who retains the secret key. We provide formal statistical bounds on the validity and power of our procedure. Through an extensive suite of experiments, we demonstrate that GaussMark is reliable, efficient, and relatively robust to corruptions such as insertions, deletions, substitutions, and roundtrip translations and can be instantiated with essentially no loss in model quality.