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. Multilingual deployment exposes evaluation-design choices that are inconsequential on English but determine conclusions cross-lingually.
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:2608.29921v1 Announce Type: cross
Abstract: The output of a Language Model can be tampered with \emph{while} the model is writing it. A simple test can thus be constructed by evaluating the mod...
By Alberto Cetoli
The paper audits 51 recent multilingual evaluation benchmarks covering 242 datasets and 219 languages, focusing on coverage, representativeness, and rigor. It finds that while coverage is broad, many languages appear in only one benchmark and entire regions are underrepresented; low‑resource languages face a task equity gap. Representativeness is compromised by heavy reliance on English translations, and rigor is weakened by benchmark contamination and parallel‑corpus overlap. The authors propose concrete recommendations for building evaluation frameworks that are natively constructed, culturally grounded, contamination‑aware, and community‑aligned.
By Manan Uppadhyay, Himanshu Beniwal, Prashant Kodali, Sunayana Sitaram