NiuTrans.LMT: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915953700241408 |
|---|---|
| author | Luo, Yingfeng Xu, Ziqiang Ouyang, Yuxuan Yang, Murun Lin, Dingyang Chang, Kaiyan Zheng, Tong Li, Bei Feng, Peinan Du, Quan Xiao, Tong Zhu, Jingbo |
| author_facet | Luo, Yingfeng Xu, Ziqiang Ouyang, Yuxuan Yang, Murun Lin, Dingyang Chang, Kaiyan Zheng, Tong Li, Bei Feng, Peinan Du, Quan Xiao, Tong Zhu, Jingbo |
| contents | Large language models have significantly advanced Multilingual Machine Translation (MMT), yet scaling to many languages while keeping quality robust across directions remains challenging. In this paper, we identify a failure mode of multilingual supervised fine-tuning (SFT) on multi-way parallel data: when such data are reused symmetrically around a pivot language (e.g., English), performance on reverse directions (X $\to$ pivot) can drop substantially. We term this phenomenon Directional Degeneration and attribute it to excessive many-to-one mappings, which encourage shortcut learning. We propose Strategic Downsampling (SD), a simple yet effective method to mitigate this degeneration. In addition, we introduce Parallel Multilingual Prompting (PMP), which augments translation instructions with an auxiliary parallel sentence to promote cross-lingual transfer during training and enables optional test-time enhancement when auxiliary translations are available. We further develop \textbf{NiuTrans.LMT} (\textbf{L}arge-scale \textbf{M}ultilingual \textbf{T}ranslation, abbreviated as \textbf{LMT}), a Chinese-English-centric suite of multilingual translation models spanning four sizes (0.6B/1.7B/4B/8B) and covering 60 languages and 234 directions. Comprehensive evaluations show that LMT is competitive among open-source MMT systems, and that our 4B LMT model performs on par with or better than substantially larger baselines. We release our models and project resources to support inclusive and scalable MMT. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_07003 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NiuTrans.LMT: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs Luo, Yingfeng Xu, Ziqiang Ouyang, Yuxuan Yang, Murun Lin, Dingyang Chang, Kaiyan Zheng, Tong Li, Bei Feng, Peinan Du, Quan Xiao, Tong Zhu, Jingbo Computation and Language Large language models have significantly advanced Multilingual Machine Translation (MMT), yet scaling to many languages while keeping quality robust across directions remains challenging. In this paper, we identify a failure mode of multilingual supervised fine-tuning (SFT) on multi-way parallel data: when such data are reused symmetrically around a pivot language (e.g., English), performance on reverse directions (X $\to$ pivot) can drop substantially. We term this phenomenon Directional Degeneration and attribute it to excessive many-to-one mappings, which encourage shortcut learning. We propose Strategic Downsampling (SD), a simple yet effective method to mitigate this degeneration. In addition, we introduce Parallel Multilingual Prompting (PMP), which augments translation instructions with an auxiliary parallel sentence to promote cross-lingual transfer during training and enables optional test-time enhancement when auxiliary translations are available. We further develop \textbf{NiuTrans.LMT} (\textbf{L}arge-scale \textbf{M}ultilingual \textbf{T}ranslation, abbreviated as \textbf{LMT}), a Chinese-English-centric suite of multilingual translation models spanning four sizes (0.6B/1.7B/4B/8B) and covering 60 languages and 234 directions. Comprehensive evaluations show that LMT is competitive among open-source MMT systems, and that our 4B LMT model performs on par with or better than substantially larger baselines. We release our models and project resources to support inclusive and scalable MMT. |
| title | NiuTrans.LMT: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2511.07003 |