Aleix Sant, Jordi Luque, Carlos Escolano · 2026 · arXiv
DOI: 10.48550/arXiv.2610.29926 · open-access PDF
이 논문은 WMT 2026 Multilingual Instruction Shared Task에 대한 우리의 제출을 설명합니다.We adapt Qwen3-8B with LoRA using organizer-provided data, EuroAlpaca, a multilingual instruction dataset created through task-preserving localization, and CrossEuroAlpaca, a cross-lingual augmentation that assigns different source and target languages to instructions, contexts and responses.Our system targets open-end question answering (QA-OEG) in ten shared-task languages represented in auxiliary data.
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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