A method for improving multilingual quality and diversity of instruction fine-tuning datasets

• URL: http://arxiv.org/abs/2509.15549v1
• Date: Fri, 19 Sep 2025 03:07:59 GMT
• Title: A method for improving multilingual quality and diversity of instruction fine-tuning datasets
• Authors: Chunguang Zhao, Yilun Liu, Pufan Zeng, Yuanchang Luo, Shimin Tao, Minggui He, Weibin Meng, Song Xu, Ziang Chen, Chen Liu, Hongxia Ma, Li Zhang, Boxing Chen, Daimeng Wei,
• Abstract summary: We introduce Multilingual Data Quality and Diversity (M-DaQ) to improve Multilingual Instruction Fine-Tuning (IFT)<n>M-DaQ is a novel method for improving LLMs multilinguality by selecting high-quality and semantically diverse multilingual IFT samples.<n> Empirical results across 18 languages demonstrate that models fine-tuned with M-DaQ achieve significant performance gains over vanilla baselines over 60% win rate.
• Score: 29.07537849245622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multilingual Instruction Fine-Tuning (IFT) is essential for enabling large language models (LLMs) to generalize effectively across diverse linguistic and cultural contexts. However, the scarcity of high-quality multilingual training data and corresponding building method remains a critical bottleneck. While data selection has shown promise in English settings, existing methods often fail to generalize across languages due to reliance on simplistic heuristics or language-specific assumptions. In this work, we introduce Multilingual Data Quality and Diversity (M-DaQ), a novel method for improving LLMs multilinguality, by selecting high-quality and semantically diverse multilingual IFT samples. We further conduct the first systematic investigation of the Superficial Alignment Hypothesis (SAH) in multilingual setting. Empirical results across 18 languages demonstrate that models fine-tuned with M-DaQ method achieve significant performance gains over vanilla baselines over 60% win rate. Human evaluations further validate these gains, highlighting the increment of cultural points in the response. We release the M-DaQ code to support future research.

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