LangMAP: A Language-Adaptive Approach to Tokenization
Language-specific tokenizers improve tokenization quality and the downstream performance of models on those languages. However, using such a tokenizer comes at a cost: either a new model must be trained from scratch, or the vocabulary of an existing pretrained model must be adapted. We propose Language-adaptive Maximum a Posteriori (LangMAP) Tokenization, a tokenization scheme that extends the UnigramLM algorithm to the multilingual setting, producing language-specific tokenization from a single shared vocabulary. Notably, LangMAP can be used when training a multilingual language model from scratch or to adapt a pretrained model's tokenizer to individual languages without changing its voc...
| Authors | Clara Meister, Suchir Salhan, Andrzej Szablewski et al. |
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| Primary category | cs.CL |
| Published | 22 June 2026 |
| Last updated at source | 23 June 2026 |
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