Equity with Efficiency: An Empirical Study of Tokenizers for Multilingual Large Language Models

arXiv:2606.15044v1 Announce Type: new Abstract: Multilingual large language models (LLMs) depend on subword tokenization to bridge discrete text and continuous neural representation. State-of-the-art multilingual LLMs often use Byte-level Byte-Pair Encoding (BPE) tokenizers that structurally favor high-resource languages and Latin scripts. For speakers of underrepresented languages, particularly those across Southeast Asia, this bias inflates inference costs and widens cross-lingual capability gaps. We present the first systematic comparison of equitable tokenizers on a unified benchmark spann

Source: arXiv cs.CL — read the full report at the original publisher.

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