XDoGE: Multilingual Data Reweighting to Enhance Language Inclusivity in LLMs
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| Format: | Preprint |
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2025
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| _version_ | 1866912757976137728 |
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| author | Lacunza, Iñaki Saiz, José Javier Shvets, Alexander Gonzalez-Agirre, Aitor Villegas, Marta |
| author_facet | Lacunza, Iñaki Saiz, José Javier Shvets, Alexander Gonzalez-Agirre, Aitor Villegas, Marta |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_10545 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | XDoGE: Multilingual Data Reweighting to Enhance Language Inclusivity in LLMs Lacunza, Iñaki Saiz, José Javier Shvets, Alexander Gonzalez-Agirre, Aitor Villegas, Marta Computation and Language 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. |
| title | XDoGE: Multilingual Data Reweighting to Enhance Language Inclusivity in LLMs |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2512.10545 |