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
The rapid expansion of multilingual LLMs has brought to light the inherent biases in current tokenization methods, particularly affecting underrepresented languages, prompting a need for more equitable solutions to expand LLM accessibility and performance worldwide.
Biased tokenization inflates inference costs and widens capability gaps for non-Latin script languages, hindering global AI adoption and reinforcing existing linguistic inequalities, making equitable tokenizers crucial for truly universal LLMs.
The focus is shifting from generic tokenization to linguistically aware, equitable models, which will improve LLM efficiency and performance for non-dominant languages and expand their commercial viability in diverse markets.
- · Southeast Asian language users
- · Developers of equitable tokenizers
- · LLM providers targeting global markets
- · Linguistically diverse AI research
- · LLMs with unoptimized, biased tokenizers
- · Research heavily reliant on Latin-script-centric datasets
- · Regions speaking underrepresented languages if this issue is not addressed
Improved performance and cost-efficiency for LLMs in underrepresented languages, particularly across Southeast Asia.
Increased adoption and commercialization of AI in diverse linguistic markets, fostering local innovation and reducing language barriers.
A potential shift in global AI power dynamics as non-English speaking nations gain more equitable access to advanced AI capabilities, potentially leading to 'Sovereign AI' initiatives.
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Read at arXiv cs.CL