GaussMark: A Practical Approach for Structural Watermarking of Language Models

Adam Block, Alexander Rakhlin, Ayush Sekhari
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:4507-4569, 2025.

Abstract

Watermarking, the process by which Large Language Model (LLM) servers imbed an imperceptible signal at inference time in order to detect text generated by their own models, has grown in importance due to the significant improvements in natural language processing tasks by modern LLMs. Current approaches are often impractical due to generation latency, detection time, degradation in text quality, or robustness; such problems often arise due to 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, 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 provide formal statistical bounds on the validity and power of our procedure and, through an extensive suite of experiments, demonstrate that GaussMark is reliable, efficient, relatively robust to corruption, and can be instantiated with essentially no loss in model quality.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-block25a, title = {{G}auss{M}ark: A Practical Approach for Structural Watermarking of Language Models}, author = {Block, Adam and Rakhlin, Alexander and Sekhari, Ayush}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {4507--4569}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/block25a/block25a.pdf}, url = {https://proceedings.mlr.press/v267/block25a.html}, abstract = {Watermarking, the process by which Large Language Model (LLM) servers imbed an imperceptible signal at inference time in order to detect text generated by their own models, has grown in importance due to the significant improvements in natural language processing tasks by modern LLMs. Current approaches are often impractical due to generation latency, detection time, degradation in text quality, or robustness; such problems often arise due to 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, 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 provide formal statistical bounds on the validity and power of our procedure and, through an extensive suite of experiments, demonstrate that GaussMark is reliable, efficient, relatively robust to corruption, and can be instantiated with essentially no loss in model quality.} }
Endnote
%0 Conference Paper %T GaussMark: A Practical Approach for Structural Watermarking of Language Models %A Adam Block %A Alexander Rakhlin %A Ayush Sekhari %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-block25a %I PMLR %P 4507--4569 %U https://proceedings.mlr.press/v267/block25a.html %V 267 %X Watermarking, the process by which Large Language Model (LLM) servers imbed an imperceptible signal at inference time in order to detect text generated by their own models, has grown in importance due to the significant improvements in natural language processing tasks by modern LLMs. Current approaches are often impractical due to generation latency, detection time, degradation in text quality, or robustness; such problems often arise due to 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, 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 provide formal statistical bounds on the validity and power of our procedure and, through an extensive suite of experiments, demonstrate that GaussMark is reliable, efficient, relatively robust to corruption, and can be instantiated with essentially no loss in model quality.
APA
Block, A., Rakhlin, A. & Sekhari, A.. (2025). GaussMark: A Practical Approach for Structural Watermarking of Language Models. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:4507-4569 Available from https://proceedings.mlr.press/v267/block25a.html.

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