An Empirical Study of Instruction-tuning Large Language Models in Chinese

• URL: http://arxiv.org/abs/2310.07328v2
• Date: Fri, 20 Oct 2023 08:02:09 GMT
• Title: An Empirical Study of Instruction-tuning Large Language Models in Chinese
• Authors: Qingyi Si, Tong Wang, Zheng Lin, Xu Zhang, Yanan Cao, Weiping Wang
• Abstract summary: This paper makes an in-depth empirical study of instruction-tuning LLMs in Chinese, which can serve as a cookbook. Specifically, we systematically explore the impact of LLM bases, parameter-efficient methods, instruction data types. We also conduct experiment to study the impact of other factors, e.g., chain-of-thought data and human-value alignment.
• Score: 32.5288378307064
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The success of ChatGPT validates the potential of large language models (LLMs) in artificial general intelligence (AGI). Subsequently, the release of LLMs has sparked the open-source community's interest in instruction-tuning, which is deemed to accelerate ChatGPT's replication process. However, research on instruction-tuning LLMs in Chinese, the world's most spoken language, is still in its early stages. Therefore, this paper makes an in-depth empirical study of instruction-tuning LLMs in Chinese, which can serve as a cookbook that provides valuable findings for effectively customizing LLMs that can better respond to Chinese instructions. Specifically, we systematically explore the impact of LLM bases, parameter-efficient methods, instruction data types, which are the three most important elements for instruction-tuning. Besides, we also conduct experiment to study the impact of other factors, e.g., chain-of-thought data and human-value alignment. We hope that this empirical study can make a modest contribution to the open Chinese version of ChatGPT. This paper will release a powerful Chinese LLMs that is comparable to ChatGLM. The code and data are available at https://github.com/PhoebusSi/Alpaca-CoT.

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