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arXiv · 2502.13647

Instruction Tuning on Public Government and Cultural Data for Low-Resource Language: a Case Study in Kazakh

Nurkhan Laiyk·Daniil Orel·Rituraj Joshi·Maiya Goloburda·Yuxia Wang·Preslav Nakov·Fajri Koto
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Abstract

Instruction tuning in low-resource languages remains underexplored due to limited text data, particularly in government and cultural domains. To address this, we introduce and open-source a large-scale (10,600 samples) instruction-following (IFT) dataset, covering key institutional and cultural knowledge relevant to Kazakhstan. Our dataset enhances LLMs' understanding of procedural, legal, and structural governance topics. We employ LLM-assisted data generation, comparing open-weight and closed-weight models for dataset construction, and select GPT-4o as the backbone. Each entity of our dataset undergoes full manual verification to ensure high quality. We also show that fine-tuning Qwen, Falcon, and Gemma on our dataset leads to consistent performance improvements in both multiple-choice and generative tasks, demonstrating the potential of LLM-assisted instruction tuning for low-resource languages.

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Nurkhan Laiyk, Daniil Orel, Rituraj Joshi, Maiya Goloburda, Yuxia Wang, Preslav Nakov, Fajri Koto. 2025-02-19. Instruction Tuning on Public Government and Cultural Data for Low-Resource Language: a Case Study in Kazakh. https://arxiv.org/abs/2502.13647

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