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arXiv Computation and Language By Franck Signe, Hippolyte Pilchen, Fran\c{c}ois Yvon, \'Edouard Grave

To Each Language Its Tokenizer: Modular Tokenizers for Efficient Multilingual LLMs

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The paper proposes a modular tokenizer framework for multilingual large language models, allowing the creation of language‑specific subtokenizers that match monolingual compression quality. It introduces a pretraining strategy that samples these subtokenizers to limit predictions to relevant vocabularies, enabling efficient training and inference. This approach reduces memory usage and speeds up inference without compromising performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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