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

Harnessing the Power of David against Goliath: Exploring Instruction Data Generation without Using Closed-Source Models