Language Models are not Equally Robust to Non-Canonical Tokenization across Languages

• URL: http://arxiv.org/abs/2607.26831v1
• Date: Wed, 29 Jul 2026 12:23:15 GMT
• Title: Language Models are not Equally Robust to Non-Canonical Tokenization across Languages
• Abstract summary: We study the behavior of language models under non-canonical tokenizations across diverse languages.<n>We show that tokenization robustness is not a universal property of language models, but depends strongly on the language and its interaction with the tokenizer.
• Score: 29.183380954421242
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Despite the existence of exponentially many valid tokenizations for a given string, language models operate on a single canonical sequence deterministically produced by the tokenizer, leaving the broader tokenization space largely uncharacterized. In this paper, we investigate this overlooked space by studying the behavior of language models under non-canonical tokenizations across diverse languages. For English, prior work shows that models are largely invariant to alternative tokenizations that represent the same underlying string. We ask whether this invariance generalizes to other languages beyond English. We conduct a multilingual study across 27 languages spanning diverse scripts and evaluate LLM behavior under alternative tokenizations across six downstream tasks. We find that tokenization invariance does not generalize: model behavior varies substantially across languages with instruction-tuned models exhibiting an average relative performance drop of 23.7% for Llama-3.1-8B, 11.4% for Qwen3-8B, and 9.9% for Gemma-3-12B. The variation of tokenization invariance is systematic across languages. Languages that exhibit higher token fragmentation show significantly greater sensitivity to non-canonical tokenizations. Our study of tokenization robustness serves as a diagnostic of how tightly a model is coupled to its tokenizer. These results demonstrate that tokenization robustness is not a universal property of language models, but depends strongly on the language and its interaction with the tokenizer. We also show that LoRA fine-tuning with multi-tokenization training data provides an effective mitigation for tokenization sensitivity. Fine-tuning on English alone improves tokenization robustness across languages, while systematically sampling diverse non-canonical tokenizations achieves the strongest overall performance.

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