Multilingual Pretraining Using a Large Corpus Machine-Translated from a Single Source Language

• URL: http://arxiv.org/abs/2410.23956v2
• Date: Wed, 06 Nov 2024 01:35:36 GMT
• Title: Multilingual Pretraining Using a Large Corpus Machine-Translated from a Single Source Language
• Authors: Jiayi Wang, Yao Lu, Maurice Weber, Max Ryabinin, Yihong Chen, Raphael Tang, Pontus Stenetorp,
• Abstract summary: Machine-translated text from a single high-quality source language can contribute significantly to the pretraining of multilingual models. We show that CuatroLLM matches or outperforms state-of-the-art multilingual models trained using closed data. We release our corpus, models, and training pipeline under open licenses at hf.co/britllm/CuatroLLM.
• Score: 34.54405113575568
• License:
• Abstract: English, as a very high-resource language, enables the pretraining of high-quality large language models (LLMs). The same cannot be said for most other languages, as leading LLMs still underperform for non-English languages, likely due to a gap in the quality and diversity of the available multilingual pretraining corpora. In this work, we find that machine-translated text from a single high-quality source language can contribute significantly to the pretraining of multilingual LLMs. We translate FineWeb-Edu, a high-quality English web dataset, into French, German, and Spanish, resulting in a final 300B-token dataset, which we call TransWeb-Edu, and pretrain a 1.3B-parameter model, CuatroLLM, from scratch on this dataset. Across five non-English reasoning tasks, we show that CuatroLLM matches or outperforms state-of-the-art multilingual models trained using closed data, such as Llama3.2 and Gemma2, despite using an order of magnitude less data, such as about 6% of the tokens used for Llama3.2's training. We further demonstrate that with additional domain-specific pretraining, amounting to less than 1% of TransWeb-Edu, CuatroLLM surpasses the state of the art in multilingual reasoning. To promote reproducibility, we release our corpus, models, and training pipeline under open licenses at hf.co/britllm/CuatroLLM.

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