Aleix Sant, Jordi Luque, Carlos Escolano · 2026 · arXiv
DOI: 10.48550/arXiv.2610.29926 · open-access PDF
本文描述了我们向 WMT 2026 多语言指令共享任务的提交情况。我们使用组织者提供的数据、通过任务维护定位创建的多语言指令数据集 EuroAlpaca 和 CrossEuroAlpaca 来调整 Qwen3-8B 与 LoRA,以及跨语言扩展,将不同的源语言和目标语言分配给指令、背景和响应。
Machine translation (Meta M2M100); the English original follows.
This paper describes our submission to the WMT 2026 Multilingual Instruction Shared Task. We adapt Qwen3-8B with LoRA using organiser-provided data, EuroAlpaca, a multilingual instruction dataset created through task-preserving localisation, and CrossEuroAlpaca, a cross-lingual augmentation that assigns different source and target languages to instructions, contexts and responses. Our system targets open-ended question answering (QA-OEG) in ten shared-task languages represented in the auxiliary data. Under matched decoding, adaptation improves the mean Gemma-4 judge score on target-language QA
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