@inproceedings{kanjirangat-etal-2026-evaluating,
    title = "Evaluating Multilingual Tokenization under Worst-N Parity-Aware {BPE}",
    author = "Kanjirangat, Vani  and
      Kletz, David  and
      Samardzic, Tanja  and
      Dolamic, Ljiljana  and
      Rinaldi, Fabio",
    editor = "Huang, Kaiyu  and
      Mo, Fengran  and
      Chen, Pinzhen  and
      Jiang, Meng",
    booktitle = "Proceedings of the 1st Workshop on Multilinguality in the Era of Large Language Models ({M}e{LLM} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.mellm-1.21/",
    pages = "221--228",
    ISBN = "979-8-89176-430-9",
    abstract = "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 tokenization 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 $N$."
}