Instruction Tuning with GPT-4

Authors: Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, Jianfeng Gao

8 pages. Work in progress. Project page:
License: CC BY-NC-SA 4.0

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.

Submitted to arXiv on 06 Apr. 2023

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