Multilingual Language Model Pretraining using Machine-translated Data

• URL: http://arxiv.org/abs/2502.13252v1
• Date: Tue, 18 Feb 2025 19:27:53 GMT
• Title: Multilingual Language Model Pretraining using Machine-translated Data
• Authors: Jiayi Wang, Yao Lu, Maurice Weber, Max Ryabinin, David Adelani, Yihong Chen, Raphael Tang, Pontus Stenetorp,
• Abstract summary: We translate FineWeb-Edu, a high-quality English web dataset, into nine languages. We show that TransWebLLM matches or outperforms state-of-the-art multilingual models trained using closed data.
• Score: 33.373858866989536
• License:
• Abstract: High-resource languages such as English, enables the pretraining of high-quality large language models (LLMs). The same can not be said for most other languages as 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 texts from a single high-quality source language can contribute significantly to the pretraining quality of multilingual LLMs. We translate FineWeb-Edu, a high-quality English web dataset, into nine languages, resulting in a 1.7-trillion-token dataset, which we call TransWebEdu and pretrain a 1.3B-parameter model, TransWebLLM, from scratch on this dataset. Across nine non-English reasoning tasks, we show that TransWebLLM matches or outperforms state-of-the-art multilingual models trained using closed data, such as Llama3.2, Qwen2.5, and Gemma, despite using an order of magnitude less data. We demonstrate that adding less than 5% of TransWebEdu as domain-specific pretraining data sets a new state-of-the-art in Arabic, Italian, Indonesian, Swahili, and Welsh understanding and commonsense reasoning tasks. To promote reproducibility, we release our corpus, models, and training pipeline under Open Source Initiative-approved licenses.

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