Improving Cross-Task Generalization with Step-by-Step Instructions
Yang Wu Yanyan Zhao Zhongyang Li Bing Qin Kai Xiong
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.