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This research presents LuxInstruct, a novel dataset designed to enhance large language models for Luxembourgish by leveraging aligned data from English, French, and German rather than relying on error-prone machine translations. The study demonstrates that this cross-lingual approach significantly improves representational alignment and generative capabilities in low-resource languages while preserving cultural nuances. This work advances the field of multilingual AI development by offering a robust methodology for curating high-quality instruction data without introducing semantic drift common in automated translation pipelines.
LuxInstruct: A Cross-Lingual Instruction Tuning Dataset For Luxembourgish
arXiv:2510.07074v2 Announce Type: replace-cross Abstract: 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.
Abstract: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.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2510.07074 [cs.CL]
(or arXiv:2510.07074v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2510.07074 Focus to learn more arXiv-issued DOI via DataCite
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