arXiv Computation and Language September 7, 2026 By Jonas Sch\"afer, Cezary Pilaszewicz, Gerhard Wunder

Robust Text Watermarking for Large Language Models via Dual Semantic Embeddings

Read the original on arXiv Computation and Language →

The paper introduces Dual-Embedding Watermarking (DEW), a semantic watermarking technique for large language models that combines contextual and token-level embeddings. DEW applies algebraic vector-space operations to generate a watermark signal that remains robust to paraphrasing and translation, while obfuscating the signal with pseudo-random matrices seeded by a secret key. Experiments demonstrate state‑of‑the‑art robustness, especially against translation, with minimal computational overhead and preserved text quality at lower watermark strengths.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

arXiv Computation and Language

Sep 14

Is Multilingual LLM Watermarking Truly Multilingual? Scaling Robustness to 100+ Languages via Back-Translation

arXiv:2510.18019v3 Announce Type: replace Abstract: Multilingual watermarking aims to make large language model (LLM) outputs traceable across languages, yet current methods still fall short. Despite...

By Asim Mohamed, Martin Gubri

llms nlp

arXiv Machine Learning

Aug 31

OpenStamp: A Watermark for Open-Source Language Models

arXiv:2608.27899v1 Announce Type: cross Abstract: With the growing prevalence of large language model (LLM) generated content, watermarking is considered a promising approach for attributing text to...

By Miroojin Bakshi, Saksham Rastogi, Danish Pruthi

llms fine-tuning

arXiv AI

5d ago

Watermarking Diffusion Language Models

The paper introduces the first watermark designed specifically for diffusion language models (DLMs), which generate tokens in arbitrary order unlike traditional autoregressive models. It overcomes the challenge of missing prior tokens by applying the watermark in expectation over the context and promoting tokens that strengthen the watermark when used as context. Experiments show a >99% true positive rate with minimal quality loss and comparable robustness to existing autoregressive watermarks.

By Thibaud Gloaguen, Robin Staab, Nikola Jovanovi\'c, Martin Vechev

llms diffusion

arXiv Computation and Language

Sep 15

DenMark: Robust Semantic Watermarking for Diffusion Language Models

arXiv:2609.14257v1 Announce Type: new Abstract: Semantic text watermarks encode signals in meaning rather than surface token choices, offering robustness to paraphrasing and other semantic-preserving...

By Tianhao Ma, Weihao Xuan, Dong-Dong Wu, Farshid Nooshi, Takashi Ishida, Gang Niu, Naoto Yokoya, Masashi Sugiyama

llms diffusion

arXiv AI

Aug 14

Security and Detectability Analysis of Unicode Text Watermarking Methods against Large Language Models

arXiv:2512. 13325v2 Announce Type: replace-cross Abstract: Securing digital text is becoming increasingly relevant due to the widespread use of large language models.

By Malte Hellmeier

llms rag

arXiv Machine Learning

Aug 17

Semantic Differentiation for Tackling Challenges in Watermarking Low-Entropy Constrained Generation Outputs

arXiv:2601. 11629v2 Announce Type: replace-cross Abstract: We demonstrate that while the current approaches for language model watermarking are effective for open-ended generation, they are inadequate at watermarking LM outputs for constrained generation tasks with low-entropy output spaces.

By Nghia T. Le, Alan Ritter, Kartik Goyal