arXiv 2403.180582024-11-02cs.CLcs.AI

COIG-CQIA：中文指令微调中质量是核心要素

COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning

Shenzhen Institute of Advanced Technology, CAS（中国科学院深圳先进技术研究院）；M-A-P；Institute of Automation, CAS（中国科学院自动化研究所）；Peking University（北京大学）；University of Science and Technology of China（中国科学技术大学）；HKUST（香港科技大学）；University of Manchester（曼彻斯特大学）；University of Waterloo（滑铁卢大学）；Vector Institute（向量研究所）

Yuelin Bai, Xinrun Du, Yiming Liang, Yonggang Jin, Junting Zhou, Ziqiang Liu, Feiteng Fang, Mingshan Chang, Tianyu Zheng, Xincheng Zhang, Nuo Ma, Zekun Wang, Ru… 展开作者

Yuelin Bai, Xinrun Du, Yiming Liang, Yonggang Jin, Junting Zhou, Ziqiang Liu, Feiteng Fang, Mingshan Chang, Tianyu Zheng, Xincheng Zhang, Nuo Ma, Zekun Wang, Ruibin Yuan, Haihong Wu, Hongquan Lin, Wenhao Huang, Jiajun Zhang, Chenghua Lin, Jie Fu, Min Yang, Shiwen Ni, Ge Zhang

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AI总结：

针对中文指令微调数据集适配性不足的问题，研究团队推出经人工校验的真实场景中文指令微调数据集COIG-CQIA，实验证明其训练的模型性能优异，还为相关数据集设计与数据混合策略提供了参考。

摘要：

英文指令微调已取得显著进展，提升了大语言模型（LLM）的效果与可靠性。然而中文指令微调领域仍存在明显差距，中文复杂的语言特征带来了巨大挑战。现有数据集大多从以英文为核心的LLM中蒸馏得到，与中文用户的交互模式适配度不高。为填补这一空白，我们推出COIG-CQIA——一个全新的中文指令微调数据集，其源自各类真实场景资源，并经过严格的人工校验。我们在COIG-CQIA上开展了大量实验，将其与强基线模型及数据集进行对比。实验结果表明，基于COIG-CQIA训练的模型在多项基准测试中取得了极具竞争力的表现。此外，我们的研究发现为设计高效的中文指令微调数据集及数据混合策略提供了若干洞见。我们的数据集可在https://huggingface.co/datasets/m-a-p/COIG-CQIA获取。

英文摘要：

Remarkable progress on English instruction tuning has facilitated the efficacy and reliability of large language models (LLMs). However, there remains a noticeable gap in instruction 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 resources and undergoing rigorous 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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。