Is Multilingual LLM Watermarking Truly Multilingual? A Simple
  Back-Translation Solution
Authors: name: Asim Mohamed, name: Martin Gubri
Organization: Parameter Lab

Multilingual watermarking aims to make large language model (LLM) outputs
traceable across languages, yet current methods still fall short. Despite
claims of cross-lingual robustness, they are evaluated only on high-resource
languages. We show that existing multilingual watermarking methods are not
truly multilingual: they fail to remain robust under translation attacks in
medium- and low-resource languages. We trace this failure to semantic
clustering, which fails when the tokenizer vocabulary contains too few
full-word tokens for a given language. To address this, we introduce STEAM, a
back-translation-based detection method that restores watermark strength lost
through translation. STEAM is compatible with any watermarking method, robust
across different tokenizers and languages, non-invasive, and easily extendable
to new languages. With average gains of +0.19 AUC and +40%p TPR@1% on 17
languages, STEAM provides a simple and robust path toward fairer watermarking
across diverse languages.