  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. 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 as well as unseen composite tasks such as multilingual summarization.
Overall, our study find that simple CoI tuning of existing instruction data can
provide consistent generalization to solve more complex, unseen, and longer
chains of instructions.
