Fine-tuning large language models (LLMs) with a collection of large and
diverse instructions has improved the model's generalization to different
tasks, even for unseen tasks. However, most existing instruction datasets
include only single instructions, and they struggle to follow complex
instructions composed of multiple subtasks (Wang et al., 2023a). In this work,
we propose a novel concept of compositional instructions called
chain-of-instructions (CoI), where the output of one instruction becomes an
input for the next like a chain. Unlike the conventional practice of solving
single instruction tasks, our proposed method encourages a model to solve each
subtask step by step until the final answer is reached. CoI-tuning (i.e.,
fine-tuning with CoI instructions) improves the model's ability to handle
instructions composed of multiple subtasks. CoI-tuned models also outperformed
baseline models on multilingual summarization, demonstrating the
generalizability of CoI models on unseen composite downstream tasks.

改进大型语言模型的细化调整方法，通过使用多样的组合指令集合，提高了模型对不同任务的泛化性能；引入了链式指令的新概念（CoI），使模型能够处理由多个子任务组成的复合指令，将模型性能在多语言摘要生成任务中进行了验证。