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Training large language models (LLMs) with open-domain instruction followingdata brings colossal success. However, manually creating such instruction datais very time-consuming and labor-intensive. Moreover, humans may struggle toproduce high-complexity instructions. In this paper, we show an avenue forcreating large amounts of instruction data with varying levels of complexityusing LLM instead of humans. Starting with an initial set of instructions, weuse our proposed Evol-Instruct to rewrite them step by step into more complexinstructions. Then, we mix all generated instruction data to fine-tune LLaMA.We call the resulting model WizardLM. Human evaluations on acomplexity-balanced test bed and Vicuna's testset show that instructions fromEvol-Instruct are superior to human-created ones. By analyzing the humanevaluation results of the high complexity part, we demonstrate that outputsfrom our WizardLM are preferred to outputs from OpenAI ChatGPT. In GPT-4automatic evaluation, WizardLM achieves more than 90\% capacity of ChatGPT on17 out of 29 skills. Even though WizardLM still lags behind ChatGPT in someaspects, our findings suggest that fine-tuning with AI-evolved instructions isa promising direction for enhancing LLMs. Our code and data are public athttps://github.com/nlpxucan/WizardLM

WizardLM: Empowering Large Language Models to Follow Complex Instructions