Is Multilingual LLM Watermarking Truly Multilingual? Scaling Robustness to 100+ Languages via Back-Translation
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Computer Science > Computation and Language
Title:Is Multilingual LLM Watermarking Truly Multilingual? Scaling Robustness to 100+ Languages via Back-Translation
Abstract: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 detection method that uses Bayesian optimisation to search among 126 candidate languages for the back-translation that best recovers the watermark strength. It is compatible with any watermarking method, robust across different tokenizers and languages, non-invasive, and easily extendable to new languages. With average gains of +0.23 AUC and +37%p TPR@1%, STEAM provides a scalable approach toward fairer watermarking across the diversity of languages.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2510.18019 [cs.CL] |
| (or arXiv:2510.18019v3 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2510.18019
arXiv-issued DOI via DataCite
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Submission history
From: Asim Mohamed [view email][v1] Mon, 20 Oct 2025 18:51:20 UTC (899 KB)
[v2] Wed, 25 Mar 2026 17:52:45 UTC (987 KB)
[v3] Fri, 11 Sep 2026 17:40:56 UTC (1,060 KB)
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