  Fine-tuning large pre-trained language models with Evol-Instruct has achieved
encouraging results across a wide range of tasks. However, designing effective
evolving methods for instruction evolution requires substantial human
expertise. This paper proposes Auto Evol-Instruct, an end-to-end framework that
evolves instruction datasets using large language models without any human
effort. The framework automatically analyzes and summarizes suitable
evolutionary strategies for the given instruction data and iteratively improves
the evolving method based on issues exposed during the instruction evolution
process. Our extensive experiments demonstrate that the best method optimized
by Auto Evol-Instruct outperforms human-designed methods on various benchmarks,
including MT-Bench, AlpacaEval, GSM8K, and HumanEval.
