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.
翻译:Translated abstract:
指令调优被广泛认为是构建通用语言模型的关键技术,在InstructGPT(Ouyang等,2022)和ChatGPT的发布之后,受到了研究人员和公众的关注。尽管英语为基础的大规模语言模型(LLMs)取得了令人瞩目的进展,但仍未探索基于英语基础的LLMs在多语种任务中是否能像在英语任务中那样通过良好的指令调优来表现,以及我们如何构建所需的资源。为弥补这一空白,我们提出了该项目,旨在通过适应4个子任务的内在特点,采用多种方法创建一个中国指令数据集。我们收集了约20万个中文指令调优样本,并进行了手工审核以保证高质量。我们还总结了现有的英语和中文指令语料库,并简要描述了新构建的中文指令语料库的一些潜在应用。结果,中国Open Instruction通用模型(COIG)语料库在Huggingface和Github上可用,并将持续更新。