Chinese Open Instruction Generalist: A Preliminary Release
Beijing Academy of Artificial Intelligence（北京人工智能研究院）；The University of Sheffield（谢菲尔德大学）；University of Michigan Ann Arbor（密歇根大学安娜堡分校）；Dartmouth University（达特茅斯学院）；Zhejiang University（浙江大学）；Beihang University（北京航空航天大学）；Carnegie Mellon University（卡内基梅隆大学）
Ge Zhang, Yemin Shi, Ruibo Liu, Ruibin Yuan, Yizhi Li, Siwei Dong, Yu Shu, Zhaoqun Li, Zekun Wang, Chenghua Lin, Wenhao Huang, Jie Fu
指令微调被广泛认为是构建通用语言模型的关键技术，随着 InstructGPT 和 ChatGPT 的发布，它吸引了研究人员和公众的关注。尽管面向英语的大规模语言模型（LLM）取得了令人瞩目的进展，但经过精心设计的指令微调后，基于英语的基础 LLM 在多语言任务上是否能表现出与英语任务相似的性能，以及我们应如何构建微调所需的语料库，仍有待充分探索。为弥补这一空白，我们提出本项目，尝试通过适应 4 个子任务内在特性的多种方法创建中文指令数据集。我们收集了约 20 万条中文指令微调样本，并经过人工检查以保证高质量。我们还总结了现有英文和中文指令语料库，并简要描述了新构建的中文指令语料库的一些潜在应用。由此产生的 Chinese Open Instruction Generalist（COIG）语料库已在 Huggingface 和 Github 上提供，并将持续更新。
英文摘要：
Instruction tuning is widely recognized as a key technique for building generalist language models, which has attracted the attention of researchers and the public with the release of InstructGPT~\citep{ouyang2022training} and ChatGPT\footnote{\url{https://chat.openai.com/}}. Despite impressive progress in English-oriented large-scale language models (LLMs), it is still under-explored whether English-based foundation LLMs can perform similarly on multilingual tasks compared to English tasks with well-designed instruction tuning and how we can construct the corpora needed for the tuning. To remedy this gap, we propose the project as an attempt to create a Chinese instruction dataset by various methods adapted to the intrinsic characteristics of 4 sub-tasks. We collect around 200k Chinese instruction tuning samples, which have been manually checked to guarantee high quality. We also summarize the existing English and Chinese instruction corpora and briefly describe some potential applications of the newly constructed Chinese instruction corpora. The resulting \textbf{C}hinese \textbf{O}pen \textbf{I}nstruction \textbf{G}eneralist (\textbf{COIG}) corpora are available in Huggingface\footnote{\url{https://huggingface.co/datasets/BAAI/COIG}} and Github\footnote{\url{https://github.com/BAAI-Zlab/COIG}}, and will be continuously updated.
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Yu-Yue Pathology Research Center, Jinfeng Laboratory, Chongqing, China（渝粤病理研究所，金风实验室，重庆，中国）；T Magnetic Resonance Imaging Translational Medical Center, Department of Radiology, Southwest Hospital, Army Medical University, Chongqing, China（7T磁共振成像转化医学中心，放射科，西南医院，中国人民解放军军医大学，重庆，中国）
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Heidelberg University（海德堡大学）；University of Tübingen（图宾根大学）
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The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Soochow University（苏州大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；University of Science and Technology of China（中国科学技术大学）
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