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

📝 Summary:
LMT introduces new multilingual translation models covering 60 languages, centered on Chinese and English. It uses Strategic Downsampling and Parallel Multilingual Prompting to improve translation quality and cross-lingual transfer, achieving state-of-the-art performance.

🔹 Publication Date: Published on Nov 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07003
• PDF: https://arxiv.org/pdf/2511.07003
• Project Page: https://github.com/NiuTrans/LMT
• Github: https://github.com/NiuTrans/LMT

🔹 Models citing this paper:
• https://huggingface.co/NiuTrans/LMT-60-1.7B
• https://huggingface.co/NiuTrans/LMT-60-0.6B-Base
• https://huggingface.co/NiuTrans/LMT-60-0.6B

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✓ https://t.iss.one/DataScienceT

#MultilingualTranslation #LLMs #MachineTranslation #NLP #AI

✨DiscoX: Benchmarking Discourse-Level Translation task in Expert Domains

📝 Summary:
A new benchmark, DiscoX, and evaluation system, Metric-S, are introduced for discourse-level, expert Chinese-English translation. Findings show advanced LLMs still fall short of human performance, underscoring challenges in professional machine translation.

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.10984
• PDF: https://arxiv.org/pdf/2511.10984

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For more data science resources:
✓ https://t.iss.one/DataScienceT

#MachineTranslation #NLP #LLM #Benchmarking #AI