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This research investigates how scaling both model parameters and training data impacts the multilingual translation capabilities of open-source LLMs. By adapting the Gemma3 architecture through continual pretraining, the authors developed MiLMMT-46 to achieve state-of-the-art performance across 46 languages without relying on proprietary systems. The study provides critical insights into optimizing resource efficiency for diverse language coverage in next-generation translation models.

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Scaling Model and Data for Multilingual Machine Translation with Open Large Language Models

arXiv21 iulie 20262 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2602.11961v3 Announce Type: replace Abstract: Open large language models (LLMs) have demonstrated improving multilingual capabilities in recent years. In this paper, we present a study of open LLMs for multilingual machine translation (MT) across a range of languages, and investigate the effects of model scaling and data scaling when adapting open LLMs to multilingual MT through continual pretraining and instruction finetuning. Based on the Gemma3 model family, we develop MiLMMT-46, which achieves top-tier multilingual translation performance across 46 languages. Extensive experiments show that MiLMMT-46 consistently outperforms recent state-of-the-art (SOTA) models, including Seed-X, HY-MT-1.5, and TranslateGemma, and achieves competitive performance with strong proprietary systems such as Google Translate and Gemini 3 Pro. Models are released at https://huggingface.co/collections/xiaomi-research/milmmt-46. Codes are released at https://github.com/xiaomi-research/gemmax.

Abstract:Open large language models (LLMs) have demonstrated improving multilingual capabilities in recent years. In this paper, we present a study of open LLMs for multilingual machine translation (MT) across a range of languages, and investigate the effects of model scaling and data scaling when adapting open LLMs to multilingual MT through continual pretraining and instruction finetuning. Based on the Gemma3 model family, we develop MiLMMT-46, which achieves top-tier multilingual translation performance across 46 languages. Extensive experiments show that MiLMMT-46 consistently outperforms recent state-of-the-art (SOTA) models, including Seed-X, HY-MT-1.5, and TranslateGemma, and achieves competitive performance with strong proprietary systems such as Google Translate and Gemini 3 Pro. Models are released at this https URL. Codes are released at this https URL.

Computation and Language (cs.CL)

arXiv:2602.11961 [cs.CL]

(or arXiv:2602.11961v3 [cs.CL] for this version)

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