Generalization to Morphologically Rich and Logographic Languages

Determine whether the Length-MAX tokenizer, which maximizes the length-weighted objective freq(t) × |t| and has been validated on English corpora (FineWeb), confers similar efficiency and performance advantages in morphologically rich languages and logographic scripts.

Background

The paper evaluates the Length-MAX tokenizer primarily on English text from FineWeb, demonstrating reductions in tokens per character (TPC), improved training and inference efficiency, memory savings, and better downstream task performance compared to BPE and other baselines. These results are grounded in the tokenizer’s length-weighted objective that favors longer, high-coverage substrings.

However, languages with rich morphology (e.g., agglutinative languages) and logographic scripts (e.g., Chinese) present different tokenization challenges and distributional properties. The authors explicitly note that it is unknown whether the observed advantages extend to these language families, motivating targeted investigations to assess generalization beyond English corpora.