@inproceedings{foroutan-etal-2026-parity,
    title = "Parity-Aware Byte-Pair Encoding: Improving Cross-lingual Fairness in Tokenization",
    author = "Foroutan, Negar  and
      Meister, Clara  and
      Paul, Debjit  and
      Niklaus, Joel  and
      Ahmadi, Sina  and
      Bosselut, Antoine  and
      Sennrich, Rico",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.342/",
    pages = "7514--7538",
    ISBN = "979-8-89176-390-6",
    abstract = "Tokenization is the first{---}and often least scrutinized{---}step of most NLP pipelines. Standard algorithms for learning tokenizers rely on frequency-based objectives, which favor languages dominant in the training data and consequently leave lower-resource languages with tokenizations that are disproportionately longer, morphologically implausible, or even riddled with {\ensuremath{<}}UNK{\ensuremath{>}} placeholders. This phenomenon ultimately amplifies computational and financial inequalities between users from different language backgrounds. To remedy this, we introduce Parity-aware Byte Pair Encoding (BPE), a variant of the widely-used BPE algorithm. At every merge step, Parity-aware BPE applies a fair-max rule that maximizes the compression gain of the currently worst-compressed language, trading a small amount of global compression for cross-lingual parity. We find empirically that Parity-aware BPE reduces tokenization inequality{---}operationalized by the Gini coefficient of per-language token costs{---}by up to 89{\%} relative to Classical BPE. This comes with negligible impact on global compression rate and no evidence of systematic degradation in downstream LM performance."
}