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.∗Equal Contribution111https://instruction-tuning-with-gpt-4.github.io/Note: This is a preliminary release, and we will continue to expand the dataset and will finetune larger models.