Instruction tuning is widely recognized as a key technique for building generalist language models, which comes to the attention of researchers and the public with the release of InstructGPT \cite{ouyang2022training} and ChatGPT [ https://chat.openai.com/ ]. Despite impressive progress in English-oriented large-scale language models (\textbf{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 brief some potential applications of the newly constructed Chinese instruction corpora.
翻译: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 和ChatGPT的发布中引起了研究人员和公众的关注。\cite{ouyang2022training} 尽管在英语取向的大规模语言模型(\textbf{LLMs})方面取得了令人印象深刻的进展,但对于基于英语基础 LLMs是否可以通过精心设计指令调优在多语言任务上与英语任务表现类似以及如何构建所需要的语料库仍未得到充分探索。为弥补这一差距,我们提出了该计划,试图通过适应4个子任务的内在特点来创建一个中文指令数据集的方法。我们收集了约200k个中文指令调优样本,并进行了人工检查以保证高质量。我们还总结了现有的英文和中文指令语料库,并简要介绍了新构建的中文指令语料库的一些潜在应用。