Beyond English: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs

• URL: http://arxiv.org/abs/2511.07003v1
• Date: Mon, 10 Nov 2025 11:54:53 GMT
• Title: Beyond English: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs
• Authors: Yingfeng Luo, Ziqiang Xu, Yuxuan Ouyang, Murun Yang, Dingyang Lin, Kaiyan Chang, Tong Zheng, Bei Li, Peinan Feng, Quan Du, Tong Xiao, Jingbo Zhu,
• Abstract summary: Large language models have significantly advanced Multilingual Machine Translation (MMT), yet the broad language coverage, consistent translation quality, and English-centric bias remain open challenges.<n>We introduce textbfLMT, a suite of textbfLarge-scale textbfMultilingual textbfTranslation models centered on both Chinese and English.<n>LMT achieves SOTA performance among models of comparable language coverage, with our 4B model surpassing the much larger Aya-13B and NLLB-54B models by a substantial margin.
• Score: 41.53385639669034
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models have significantly advanced Multilingual Machine Translation (MMT), yet the broad language coverage, consistent translation quality, and English-centric bias remain open challenges. To address these challenges, we introduce \textbf{LMT}, a suite of \textbf{L}arge-scale \textbf{M}ultilingual \textbf{T}ranslation models centered on both Chinese and English, covering 60 languages and 234 translation directions. During development, we identify a previously overlooked phenomenon of \textbf{directional degeneration}, where symmetric multi-way fine-tuning data overemphasize reverse directions (X $\to$ En/Zh), leading to excessive many-to-one mappings and degraded translation quality. We propose \textbf{Strategic Downsampling}, a simple yet effective method to mitigate this degeneration. In addition, we design \textbf{Parallel Multilingual Prompting (PMP)}, which leverages typologically related auxiliary languages to enhance cross-lingual transfer. Through rigorous data curation and refined adaptation strategies, LMT achieves SOTA performance among models of comparable language coverage, with our 4B model (LMT-60-4B) surpassing the much larger Aya-101-13B and NLLB-54B models by a substantial margin. We release LMT in four sizes (0.6B/1.7B/4B/8B) to catalyze future research and provide strong baselines for inclusive, scalable, and high-quality MMT \footnote{\href{https://github.com/NiuTrans/LMT}{https://github.com/NiuTrans/LMT}}.

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