Parity-Aware Byte-Pair Encoding: Improving Cross-lingual Fairness in Tokenization
Negar Foroutan Google, Clara Meister, Debjit Paul Microsoft Research, Joel Niklaus Hugging Face, Sina Ahmadi University of Zurich, Antoine Bosselut EPFL, Rico Sennrich University of Zurich
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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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表