Scaling Model and Data for Multilingual Machine Translation with Open Large Language Models
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 compe...
| Authors | Yuzhe Shang, Pengzhi Gao, Wei Liu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 12 February 2026 |
| Last updated at source | 20 July 2026 |
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