为卢森堡语构建跨语言指令数据集，提升低资源语言模型性能。
LuxInstruct: A Cross-Lingual Instruction Tuning Dataset For Luxembourgish
- 利用英法德三语对齐数据构建卢森堡语指令数据，避免机器翻译误差。
- 跨语言指令微调显著提升卢森堡语生成能力与多语言表征对齐。
- 适合关注低资源语言、跨语言模型与数据质量的研究者。
指令微调已成为提升大语言模型性能的关键技术，使其更准确响应人类提示。然而，卢森堡语等低资源语言因缺乏高质量指令数据而受限，传统依赖机器翻译常引入语义偏差和文化失真。本文提出一种无需机器翻译的跨语言指令微调数据集构建方法，基于英语、法语和德语的对齐数据，构建高质量卢森堡语数据集，保留语言与文化细节。实验证明，跨语言指令微调不仅能增强多语言间表征对齐，还能显著提升模型在卢森堡语中的生成能力，表明优质跨语言数据构建可规避机器翻译的常见问题，直接推动低资源语言发展。
原文摘要 · Abstract (English)
Instruction tuning has become a key technique for enhancing the performance of large language models, enabling them to better follow human prompts. However, low-resource languages such as Luxembourgish face severe limitations due to the lack of high-quality instruction datasets. Traditional reliance on machine translation often introduces semantic misalignment and cultural inaccuracies. In this work, we address these challenges by creating a cross-lingual instruction tuning dataset for Luxembourgish, without resorting to machine-generated translations into it. Instead, by leveraging aligned data from English, French, and German, we build a high-quality dataset that preserves linguistic and cultural nuances. We provide evidence that cross-lingual instruction tuning not only improves representational alignment across languages but also the model's generative capabilities in Luxembourgish. This highlights how cross-lingual data curation can avoid the common pitfalls of machine-translated data and directly benefit low-resource language development.
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