Instructions from Evol-Instruct are superior to the ones from human-created ShareGPT.
When we use the same amount of Evol-Instruct data (i.e., 70k) as Vicuna to ﬁne-tune LLaMA
7B, our model WizardLM signiﬁcantly outperforms Vicuna, with a win rate 12.4% higher than vicuna
(41.3% vs. 28.9%).

Labelers prefer WizardLM outputs over outputs from ChatGPT under complex test instruc-
tions. On our test set, our WizardLM performs worse than ChatGPT, with a win rate 12.8% lower
than Chatgpt (28.0% vs. 40.8%). However, in the high-difﬁculty section of our test set (difﬁculty
level ≥ 8), our WizardLM even outperforms ChatGPT, with a win rate 7.9% larger than Chatgpt
(42.9% vs. 35.0%). This indicates that our method can signiﬁcantly improve the ability of large
language models to handle complex instructions.

2 Related Work
WizardLM- Empowering Large Language Models to Follow Complex Instructions