Language-Specific Effects of Tokenizer Choice in Multilingual Language Models
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Computer Science > Computation and Language
arXiv:2610.12144 (cs)
[Submitted on 8 Oct 2026]
Title:Language-Specific Effects of Tokenizer Choice in Multilingual Language Models
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Abstract:Tokenizer choice affects multilingual language modeling, but vocabulary capacity is finite and vocabulary size is often constrained: improving representation for some languages often comes at the expense of others. We therefore ask whether tokenizer choice matters equally across languages, a question that the current literature leave unanswered. To this end, we train 123 language models spanning 54 tokenizers. In the main comparison, architecture, training corpus, training-token budget, and optimization are held fixed, so the models differ only in their tokenizer. We find that tokenizer choice matters more for languages with less language-model training data: across the 54 tokenizers, the standard deviation of a language's bits-per-byte (BPB) increases as its model training-data share decreases (Spearman rho = -0.52 over the 31 trained languages and -0.69 over the 28 written with word boundaries). Leaving a language out of tokenizer training raises its BPB in every language we study, and the penalty tends to be larger for languages with less language-model training data. Giving lower-resource languages a larger share of tokenizer-training data, however, does not unconditionally help those languages: both equal weighting and an allocation inverting the shares with respect to the language model training data increase their BPB, particularly when language-model training repeats data. Finally, which intrinsic tokenizer properties are associated with better BPB differs across languages, providing further evidence that what makes a good tokenizer depends on the language. We find that the metrics quantifying these properties can be successfully used to predict downstream models' pairwise BPB rankings, suggesting a practical strategy for screening tokenizer candidates before training language models.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.12144 [cs.CL] |
| (or arXiv:2610.12144v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12144
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Language-Specific Effects of Tokenizer Choice in Multilingual Language Models, by Clara Meister and 2 other authors
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