LangMAP: A Language-Adaptive Approach to Tokenization

• URL: http://arxiv.org/abs/2606.23566v2
• Date: Tue, 23 Jun 2026 17:41:34 GMT
• Title: LangMAP: A Language-Adaptive Approach to Tokenization
• Abstract summary: LangMAP is a tokenization scheme that extends the UnigramLM algorithm to the multilingual setting.<n>It can be used when training a multilingual language model from scratch or to adapt a pretrained model's tokenizer to individual languages.<n>While language labels are required at training time, a key feature of the algorithm is that it then performs language-specific tokenization at inference without knowledge of the input's language.
• Score: 30.443524396044428
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 vocabulary. While language labels are required at training time, a key feature of the algorithm is that it then performs language-specific tokenization at inference without knowledge of the input's language. Across 14 open-source tokenizers, 9 natural languages, and 9 programming languages, LangMAP improves morphological boundary alignment and, for all coding languages tested, alignment with abstract syntax tree (AST) leaf boundaries. In fine-tuning experiments, results are mixed: LangMAP improves target-language grammatical acceptability (MultiBLiMP) on the languages tested; its benefits are less consistent on knowledge-related tasks (Global-PIQA, Belebele).

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