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This paper investigates the impact of model and data scaling on multilingual machine translation (MT) performance using open large language models (LLMs). The authors adapt Gemma3 models via continual pretraining and instruction finetuning, creating MiLMMT-46, a model covering 46 languages. Results demonstrate that MiLMMT-46 surpasses existing open-source SOTA models and rivals proprietary systems like Google Translate and Gemini 3 Pro in multilingual translation quality.
Open LLMs can now rival proprietary systems in multilingual translation, as demonstrated by MiLMMT-46's competitive performance against Google Translate and Gemini 3 Pro.
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.
