Evaluating Multilingual Tokenization under Worst-N Parity-Aware BPE
Improving the fairness of a language model is a goal that applies at every level of the model. In this paper, we evaluate a method targeting a foundational level: tokenization. We present a multilingual evaluation of parity-aware to-kenization under worst-N optimization, extending PA-BPE to jointly optimize over the N worst-compressed languages. We evaluate this formulation for N > 1 across vocabulary sizes of 16K and 32K on the languages from the flores+ benchmark, using metrics that capture both efficiency and structural alignment. Our results reveal that the effects of increasing N are inconsistent across metrics and do not lead to major gains. Efficiency-oriented and boundary-level metrics show a modest tendency to improve at higher values of N , while structural alignment metrics (such as AST alignment and boundary crossing) exhibit no clear pattern, suggesting that compression fairness and linguistic structure are mainly orthogonal objectives. Script-level analysis further reveals uneven effects across writing systems, with several non-Latin scripts showing greater sensitivity to increasing N .