The Figure 7 and Figure 8 illustrate that our automatic evolution method generated instructions that
were more difﬁcult and complex than those created by human participants in ShareGPT. Moreover, the
depth of the instructions increase signiﬁcantly with each iteration of the evolution process, indicating
the scalability of our method.
In-breadth Surpassing Human Instructions.
The objective of this study is to examine the semantic breadth of different sets of instructions from
ShareGPT, Alpaca, and our proposed data reinforcement method with four rounds. We employ the
BERT model to produce embeddings for each instruction, which capture their semantic content as
high-dimensional vectors. We then utilize the t-SNE algorithm to perform dimensionality reduction
on the embeddings from high-dimensional space to two-dimensional space, which enables us to
plot them on a plane. Lastly, we apply the k-means algorithm to partition the instructions into 20
WizardLM- Empowering Large Language Models to Follow Complex Instructions