XDoGE: Multilingual Data Reweighting to Enhance Language Inclusivity in LLMs

• URL: http://arxiv.org/abs/2512.10545v1
• Date: Thu, 11 Dec 2025 11:22:53 GMT
• Title: XDoGE: Multilingual Data Reweighting to Enhance Language Inclusivity in LLMs
• Authors: Iñaki Lacunza, José Javier Saiz, Alexander Shvets, Aitor Gonzalez-Agirre, Marta Villegas,
• Abstract summary: Current large language models (LLMs) are trained on massive amounts of text data, primarily from a few dominant languages.<n>We propose to optimize the language distribution by training a small proxy model within a domain-reweighing DoGE algorithm.<n>We then rescale the data and train a full-size model with the established language weights either from scratch or within a continual pre-training phase.
• Score: 41.71907186207218
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current large language models (LLMs) are trained on massive amounts of text data, primarily from a few dominant languages. Studies suggest that this over-reliance on high-resource languages, such as English, hampers LLM performance in mid- and low-resource languages. To mitigate this problem, we propose to (i) optimize the language distribution by training a small proxy model within a domain-reweighing DoGE algorithm that we extend to XDoGE for a multilingual setup, and (ii) rescale the data and train a full-size model with the established language weights either from scratch or within a continual pre-training phase (CPT). We target six languages possessing a variety of geographic and intra- and inter-language-family relations, namely, English and Spanish (high-resource), Portuguese and Catalan (mid-resource), Galician and Basque (low-resource). We experiment with Salamandra-2b, which is a promising model for these languages. We investigate the effects of substantial data repetition on minor languages and under-sampling on dominant languages using the IberoBench framework for quantitative evaluation. Finally, we release a new promising IberianLLM-7B-Instruct model centering on Iberian languages and English that we pretrained from scratch and further improved using CPT with the XDoGE weights.

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