AI RESEARCH
NiuTrans.LMT: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs
arXiv CS.CL
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ArXi:2511.07003v2 Announce Type: replace 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.