Instruction tuning has been shown to be able to improve cross-task generalization of language models. However, it is still challenging for language models to complete the target tasks following the instructions, as the instructions are general and lack intermediate steps. To address this problem, we propose to incorporate the step-by-step instructions to help language models to decompose the tasks, which can provide the detailed and specific procedures for completing the target tasks. The step-by-step instructions are obtained automatically by prompting ChatGPT, which are further combined with the original instructions to tune language models. The extensive experiments on SUP-NATINST show that the high-quality step-by-step instructions can improve cross-task generalization across different model sizes. Moreover, the further analysis indicates the importance of the order of steps of the step-by-step instruction for the improvement. To facilitate future research, we release the step-by-step instructions and their human quality evaluation results.

翻译：指令微调已被证明能够提升语言模型的跨任务泛化能力。然而，语言模型在遵循指令完成目标任务时仍面临挑战，因为现有指令通常较为笼统且缺乏中间步骤。为解决此问题，我们提出引入逐步指令来帮助语言模型分解任务，这些指令能为完成目标任务提供详细且具体的过程说明。逐步指令通过提示ChatGPT自动获取，并与原始指令共同用于微调语言模型。在SUP-NATINST上的大量实验表明，高质量的逐步指令能够提升不同规模语言模型的跨任务泛化能力。进一步分析揭示了逐步指令中步骤顺序对性能提升的重要性。为促进未来研究，我们开源了逐步指令及其人工质量评估结果。