通过逐步指令改进跨任务泛化
Improving Cross-Task Generalization with Step-by-Step Instructions
AI总结：
针对指令缺乏中间步骤导致跨任务泛化困难的问题，提出自动生成逐步指令辅助模型分解任务，实验证明其能提升不同规模模型的跨任务泛化性能。
摘要：
指令微调已被证明能够提高语言模型的跨任务泛化能力。然而，由于指令通常较为笼统且缺乏中间步骤，语言模型在遵循指令完成目标任务方面仍面临挑战。为解决这一问题，我们提出引入逐步指令来帮助语言模型分解任务，从而为完成目标任务提供详细且具体的步骤。逐步指令通过提示ChatGPT自动获取，并与原始指令结合以微调语言模型。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模型的跨任务泛化能力。此外，进一步的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，我们发布了逐步指令及其人工质量评估结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
