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
arXiv Computation and Language
Sep 7

Robust Text Watermarking for Large Language Models via Dual Semantic Embeddings

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.

By Jonas Sch\"afer, Cezary Pilaszewicz, Gerhard Wunder