Toward Cross-Lingual Quality Classifiers for Multilingual Pretraining Data Selection

Yassine Turki, Vinko Sabolčec, Bettina Messmer, Martin Jaggi

Keywords: large language model pretraining, data selection, multilinguality

TL;DR: We show that multilingual classifiers built on FineWeb2-HQ beat monolingual baselines across French, Spanish, Arabic & Chinese, letting high-resource languages subsidize data curation for low-resource ones.

Abstract:

As Large Language Models (LLMs) scale, data curation has shifted from maximizing volume to optimizing the signal-to-noise ratio by performing quality filtering. However, for many languages, native high-quality data is insufficient to train robust quality classifiers. This work investigates the idea that quality markers in embedding space may show cross-lingual consistency, which would allow high-resource languages to subsidize the filtering of low-resource ones.
We evaluate various filtering strategies, including cross-lingual transfer, third quartile sampling (Q3), and retention rate tuning. Our results demonstrate that massive multilingual pooling frequently outperforms monolingual baselines in both rank stability and aggregate accuracy for a 1B model trained on 103B tokens, delivering gains for high resource languages (1.2\% increase in aggregate normalized accuracy for French) and matching or exceeding monolingual baselines for low-resource languages. However, we find that scale alone does not guarantee stability. Furthermore, for high-resource languages like French, we show that refining the decision boundary through third quartile sampling (Q3) or tuning the retention rate is necessary to fully leverage the multilingual signal.

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Submission Number: 163