  Instruction tuning is instrumental in enabling Large Language Models~(LLMs)
to follow user instructions to complete various open-domain tasks. The success
of instruction tuning depends on the availability of high-quality instruction
data. Owing to the exorbitant cost and substandard quality of human annotation,
recent works have been deeply engaged in the exploration of the utilization of
powerful closed-source models to generate instruction data automatically.
However, these methods carry potential risks arising from the usage
requirements of powerful closed-source models, which strictly forbid the
utilization of their outputs to develop machine learning models. To deal with
this problem, in this work, we explore alternative approaches to generate
high-quality instruction data that do not rely on closed-source models. Our
exploration includes an investigation of various existing instruction
generation methods, culminating in the integration of the most efficient
variant with two novel strategies to enhance the quality further. Evaluation
results from two benchmarks and the GPT-4 model demonstrate the effectiveness
of our generated instruction data, which can outperform Alpaca, a method
reliant on closed-source models. We hope that more progress can be achieved in
generating high-quality instruction data without using closed-source models.
