arXiv:2608. 20047v1 Announce Type: cross Abstract: Watermarking schemes for large language model output are evaluated almost exclusively on English text using each scheme's detection threshold and a narrow set of quality measurements.
By Alexander Nemecek, Osama Zafar, Debargha Ganguly, Vikash Singh, Vipin Chaudhary, Erman Ayday
arXiv:2606. 00613v1 Announce Type: cross Abstract: Watermarking should identify language-model output without degrading quality or limiting verification to the model provider.
By Shinwoo Park, Hyejin Park, Hyeseon An, Yo-Sub Han
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
arXiv:2604.13776v3 Announce Type: replace-cross
Abstract: Watermarking is becoming the default mechanism for AI content authentication, with governance policies and frameworks referencing it as infra...
By Alexander Nemecek, Osama Zafar, Yuqiao Xu, Wenbiao Li, Erman Ayday
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
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...
By Romina Omidi, Nima Naderloui, Yuan Hong, Binghui Wang