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

• URL: http://arxiv.org/abs/2502.13647v1
• Date: Wed, 19 Feb 2025 11:44:27 GMT
• Title: Instruction Tuning on Public Government and Cultural Data for Low-Resource Language: a Case Study in Kazakh
• Authors: Nurkhan Laiyk, Daniil Orel, Rituraj Joshi, Maiya Goloburda, Yuxia Wang, Preslav Nakov, Fajri Koto,
• Abstract summary: We introduce and open-source a large-scale (10,600 samples) instruction-following dataset, covering key institutional and cultural knowledge relevant to Kazakhstan.<n>We employ LLM-assisted data generation, comparing open-weight and closed-weight models for dataset construction, and select GPT-4o as the backbone.<n>We show that fine-tuning Qwen, Falcon, and Gemma on our dataset leads to consistent performance improvements in both multiple-choice and generative tasks.
• Score: 29.813758537374795
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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