@inproceedings{bai-etal-2025-coig,
    title = "{COIG}-{CQIA}: Quality is All You Need for {C}hinese Instruction Fine-tuning",
    author = "Bai, Yuelin  and
      Du, Xeron  and
      Liang, Yiming  and
      Jin, Leo  and
      Zhou, Junting  and
      Liu, Ziqiang  and
      Fang, Feiteng  and
      Chang, Mingshan  and
      Zheng, Tianyu  and
      Zhang, Xincheng  and
      Ma, Nuo  and
      Wang, Zekun Moore  and
      Yuan, Ruibin  and
      Wu, Haihong  and
      Lin, Hongquan  and
      Huang, Wenhao  and
      Zhang, Jiajun  and
      Lin, Chenghua  and
      Fu, Jie  and
      Yang, Min  and
      Ni, Shiwen  and
      Zhang, Ge",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-naacl.457/",
    pages = "8190--8205",
    ISBN = "979-8-89176-195-7",
    abstract = "Remarkable progress on large language models (LLMs), particularly in English, has facilitated impressive capabilities in following human instructions. However, there remains a noticeable gap in instruction fine-tuning for Chinese, where the complex linguistic features pose significant challenges. Existing datasets, generally distilled from English-centric LLMs, are not well-aligned with Chinese users' interaction patterns. To bridge this gap, we introduce COIG-CQIA, a new Chinese instruction tuning dataset derived from various real-world data resources and undergoing comprehensive human verification. We conduct extensive experiments on COIG-CQIA, and compare them with strong baseline models and datasets. The experimental results show that models trained on COIG-CQIA achieve highly competitive performance in diverse benchmarks. Additionally, our findings offer several insights for designing effective Chinese instruction-tuning datasets and data mixing strategies. Our dataset are available at https://huggingface.co/datasets/m-a-p/COIG-CQIA."
}