📄 论文总结
超越英语：利用大语言模型实现包容且可扩展的多语言机器翻译 / Beyond English: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs
1️⃣ 一句话总结
这项研究提出了一套以中英双语为核心的大规模多语言翻译模型LMT，通过创新的数据平衡策略和提示方法，在覆盖60种语言时显著提升了翻译质量，有效克服了传统模型过度依赖英语的问题。
🤖 系统
Abstract - Beyond English: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs
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{this https URL}{this https URL}}.