The Tokenizer Tax Across 25 European Languages: Domain Invariance, Cross-Lingual Few-Shot Effects, and the Ukrainian Penalty
arXiv:2605.24718v1 Announce Type: new Abstract: Tokenizer fertility the number of tokens per word imposes a hidden cost on non-English NLP. We measure fertility for ten foundation models across 25 European languages on parallel text, producing the first controlled tokenizer tax map for the continent. The tax spans 2.5x from English (1.2 tokens/word) to Greek/Maltese (~3.1), following a clear hierarchy: Romance (1.5-1.7), Germanic (1.7-1.9), Slavic (2.2-2.5), Uralic/Baltic (2.7-3.0). Ukrainian (2.7) pays 15-18% more than cognate Slavic languages, reflecting underrepresentation in pre-training d
The proliferation of Large Language Models (LLMs) has amplified the previously hidden costs associated with multilingual NLP, making 'tokenizer tax' a critical consideration for equitable AI development. This research provides the first systematic quantification of this issue across Europe.
This research quantifies a critical, often overlooked, barrier to equitable and efficient non-English NLP development, directly impacting the cost, performance, and accessibility of AI for diverse linguistic groups. A strategic reader should care because it underscores the foundational dependence of AI on underlying data and highlights disparities that can exacerbate digital divides.
The explicit quantification of the 'tokenizer tax' changes the understanding of foundational model efficiency across languages, revealing that non-English languages, particularly less resourced ones like Ukrainian, incur significantly higher processing costs. This insight will likely influence model design, resource allocation, and policy for multilingual AI.
- · English NLP development
- · Model architectures optimized for multilingual efficiency
- · Policymakers advocating for linguistic equity in AI
- · Researchers focused on cross-lingual NLP
- · Non-English NLP development (current state)
- · Languages with high 'tokenizer tax' (e.g., Greek, Maltese, Ukrainian)
- · Resource-constrained countries aiming for AI self-sufficiency in their native to
- · Foundation models not optimized for multilingual efficiency
The immediate first-order effect is a clearer understanding of language-specific resource allocation needs for training and deploying multilingual AI models.
A plausible second-order consequence is the development of new tokenizer algorithms and model pre-training strategies specifically designed to reduce the 'tokenizer tax' for high-fertility languages.
A speculative but reasoned third-order consequence is national or international initiatives to fund and support increased data collection and model development for underrepresented languages to mitigate their 'tokenizer penalty' and foster linguistic AI sovereignty.
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Read at arXiv cs.CL