Majority Bit-Aware Watermarking For Large Language Models
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
The Flow has not summarised this story yet — read it at arXiv Computation and Language.
WeaveMark is a new multi‑bit watermarking scheme for large language models that improves payload capacity, extraction accuracy, and text quality by using coded payload spreading, soft‑decision error‑correcting codes, and unbiased multilayer reweighting. It also adds zero‑bit layers for reliable detection of watermark presence. Experiments demonstrate significant gains, achieving an 89.8% match rate for 32‑bit messages at 200 tokens and maintaining 86.0% accuracy under 10% substitution attacks on 16‑bit messages, far outperforming the BiMark baseline.
arXiv:2610.27079v1 Announce Type: new Abstract: Large language model (LLM) watermarking operates on the next-token distribution that evolves during generation. In streaming generation, each sampled t...
arXiv:2610.27653v1 Announce Type: cross Abstract: Watermarking has emerged as a key tool for identifying text generated by large language models (LLMs), with current schemes embedding statistical sig...
TTMark introduces a pairwise watermarking framework that extends distortion‑free watermarking from single tokens to adjacent token pairs, enlarging the watermarking alphabet from V to V². By watermarking the joint distribution of consecutive tokens, the detector can exploit both token entropy and conditional entropy while maintaining distortion‑freeness. Experiments on multiple language models and datasets show that TTMARK improves detectability, robustness to edits, and localized watermark detection without degrading generation quality.
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...
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.