Is Multilingual LLM Watermarking Truly Multilingual? A Simple Back-Translation Solution
Existing multilingual watermarking methods for large language models exhibit fragility in low- and medium-resource languages due to semantic clustering failure and are vulnerable to translation attacks, undermining cross-lingual traceability. This paper proposes STEAM, a non-intrusive watermark enhancement method based on back-translation that restores watermark signals diluted by translation via bilingual alignment and semantic consistency verification. STEAM requires no model training, is compatible with any existing watermarking scheme, and supports multiple tokenizers and rapid extension to new languages. Experiments across 17 languages demonstrate that STEAM improves average AUC by 0.19 and TPR@1% by 40 percentage points, significantly enhancing watermark robustness and traceability in low- and medium-resource languages. To our knowledge, STEAM is the first approach to achieve truly cross-lingual, highly robust multilingual content provenance.