Linguistics-Aware Non-Distortionary LLM Watermarking
Watermarking should identify language-model output without degrading quality or limiting verification to the model provider. Multilingual deployment makes this harder because morphology, segmentation, and script change where watermark evidence can be naturally embedded. We introduce LUNA, a linguistically adaptive watermark that combines model-free detection with single-token non-distortion under the standard random-key model. LUNA estimates normalized next-tag entropy from part-of-speech contexts in an external corpus and uses it to set the depth of a non-distortionary binary tournament sampler; the detector reconstructs the same schedule from text, a tokenizer, a tagger, and a secret ke...
| Authors | Shinwoo Park, Hyejin Park, Hyeseon An et al. |
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| Primary category | cs.CL |
| Published | 30 May 2026 |
| Last updated at source | 29 August 2026 |
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