Bengali's alphasyllabary script incurs a tokenization penalty: elevated token fertility fragments linguistic units and compounds the training data deficit¶

Claim · All claims
Evidence · 1 study · review r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly

Subclaims¶

q2 i? Standard BPE and WordPiece tokenizers, optimized for Latin script, fragment Bengali's matras and yuktakshar conjunct forms into more subword tokens, so equal data volumes do not yield equal representational quality. → Avijit Roy 2026

Evidence¶

Avijit Roy 2026¶

Avijit Roy, Proma Roy. (2026). Structural Silence: When AI Infrastructure Fails Speakers of Underrepresented Languages — A Case Study in Bengali, the Digital Divide, and the Hidden Cost of English-Centric Design. arXiv preprint. https://arxiv.org/abs/2608.12278 doi:10.48550/arxiv.2608.12278

q2 · i? · review · r2

Analytic synthesis citing Shahriar and Barbosa (2024), who document substantially higher Bengali fertility rates under standard BPE. The article describes fragmentation that disrupts the linguistic units models rely on; exact ratios depend on tokenizer vocabulary size.

"The practical consequence is elevated token fertility: Bengali text requires significantly more subword tokens to represent the same semantic content as comparable English text."

Discussion¶

Related Claims¶

• General-purpose large language models perform substantially worse on Bengali downstream tasks than on equivalent English tasks, with gaps consistent with data-volume explanations — related