Parity-Aware Byte-Pair Encoding: Improving Cross-lingual Fairness in Tokenization

Negar Foroutan; Clara Meister; Debjit Paul; Joel Niklaus; Sina Ahmadi; Antoine Bosselut; Rico Sennrich

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arXiv · 2508.04796

Parity-Aware Byte-Pair Encoding: Improving Cross-lingual Fairness in Tokenization

Negar Foroutan·Clara Meister·Debjit Paul·Joel Niklaus·Sina Ahmadi·Antoine Bosselut·Rico Sennrich

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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 $ $ 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.

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Negar Foroutan, Clara Meister, Debjit Paul, Joel Niklaus, Sina Ahmadi, Antoine Bosselut, Rico Sennrich. 2025-08-06. Parity-Aware Byte-Pair Encoding: Improving Cross-lingual Fairness in Tokenization. https://arxiv.org/abs/2508.04796

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