Instruction Tuning with GPT-4

Baolin Peng; Chunyuan Li; Pengcheng He; Michel Galley; Jianfeng Gao

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arXiv · 2304.03277

Instruction Tuning with GPT-4

Baolin Peng·Chunyuan Li·Pengcheng He·Michel Galley·Jianfeng Gao

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Abstract

Prior work has shown that finetuning large language models (LLMs) using machine-generated instruction-following data enables such models to achieve remarkable zero-shot capabilities on new tasks, and no human-written instructions are needed. In this paper, we present the first attempt to use GPT-4 to generate instruction-following data for LLM finetuning. Our early experiments on instruction-tuned LLaMA models show that the 52K English and Chinese instruction-following data generated by GPT-4 leads to superior zero-shot performance on new tasks to the instruction-following data generated by previous state-of-the-art models. We also collect feedback and comparison data from GPT-4 to enable a comprehensive evaluation and reward model training. We make our data generated using GPT-4 as well as our codebase publicly available.

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Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, Jianfeng Gao. 2023-04-06. Instruction Tuning with GPT-4. https://arxiv.org/abs/2304.03277

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