Is Multilingual LLM Watermarking Truly Multilingual? A Simple
  Back-Translation Solution
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.
68f89a6a7669bcaeecce0ef3
Asim Mohamed
claimed_verified
2025-10-22T15:42:36.824Z
66cfc97cbee2b9a93bcc7306
https://cdn-avatars.huggingface.co/v1/production/uploads/66cfc97cbee2b9a93bcc7306/kka3lTAsjVUpHkExBgok4.jpeg
Asim Mohamed
user
asimz
68f89a6a7669bcaeecce0ef4
Martin Gubri
claimed_verified
2025-10-22T15:42:39.466Z
63ee35c3f599efc7a010c792
https://cdn-avatars.huggingface.co/v1/production/uploads/1676555600618-noauth.jpeg
Martin Gubri
user
mgubri
Is Multilingual LLM Watermarking Truly Multilingual? A Simple
  Back-Translation Solution
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.
STEAM, a back-translation-based detection method, enhances multilingual watermarking robustness across various languages by addressing semantic clustering failures.
multilingual watermarking large language model translation attacks medium-resource languages low-resource languages semantic clustering tokenizer vocabulary full-word tokens back-translation AUC TPR@1%