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.

Auto Evol-Instruct 是一个自动分析和总结适合给定指令数据的进化策略，并通过改进指令进化过程中暴露的问题，迭代提高进化方法的端到端框架。通过广泛的实验证明，Auto Evol-Instruct 优化的最佳方法在 MT-Bench，AlpacaEval，GSM8K 和 HumanEval 等各种基准测试中优于人工设计的方法。