Latent Core Tokenizer: Compress, but Meaningfully — A Morphology-Driven Multilingual Tokenizer

Forum topic · 小凯 · 2026-10-12

Summary

Latent Core Tokenizer (LCT) is a language-agnostic tokenization approach that decouples structural discovery from vocabulary construction. Instead of optimizing purely for compression, LCT applies Minimum Description Length, entropy-based boundary signals, and morphotactic constraints to identify reusable linguistic units before building a shared 200K-token vocabulary. Evaluated across 104 languages, LCT achieves lower fertility and higher MorphScore than BPE, Unigram, and parity-aware BPE, while maintaining comparable cross-lingual disparity in tokenization cost. On four multilingual downstream benchmarks, it improves aggregate scores by 1.48, 1.83, and 2.00 points over these baselines respectively. The key finding is that compression rate alone does not predict representation quality; what matters is morphology-driven structural discovery and how frequency is used to allocate vocabulary capacity across languages. This paper (arXiv:2610.12376) offers a promising direction for building more equitable multilingual tokenizers for NLP models.

Paper Overview

Research Area: NLP Authors: Felermino D. M. A. Ali, Millicent Ochieng, Ogbemi Ekwejunor-Etchie, Ade Famoti, Jacki O'Neill, Debjit Paul Published: 2026-10-08 arXiv: 2610.12376

Key Findings

Tokenizers are commonly optimized for compression, but a compact vocabulary does not necessarily distribute its capacity evenly across languages.
The authors introduce the Latent Core Tokenizer (LCT), a language-agnostic approach that separates structural discovery from vocabulary construction.
LCT uses Minimum Description Length, entropy-based boundary signals, and morphotactic constraints to identify reusable linguistic units before constructing a shared vocabulary.
Across 104 languages with a 200K-token vocabulary, LCT achieves lower fertility and higher MorphScore than BPE, Unigram, and parity-aware BPE, while maintaining comparable cross-lingual disparity in tokenization cost.
On four multilingual downstream benchmarks, LCT improves aggregate scores by 1.48, 1.83, and 2.00 points over BPE, Unigram, and parity-aware BPE, respectively.

Takeaway

Compression rate alone does not predict representation quality. The critical factors are morphology-driven structural discovery and how frequency is used to allocate the final vocabulary across languages.

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