# Instruction Tuning with GPT-4

**Authors:** Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, Jianfeng Gao  
**arXiv:** [2304.03277](https://arxiv.org/abs/2304.03277)  
**Date:** 2023-04-06  
**Summary page:** https://www.summarizepaper.com/fr/arxiv-id/2304.03277v1/

## 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.