The dominant paradigm for instruction tuning is the random-shuffled training
of maximally diverse instruction-response pairs. This paper explores the
potential benefits of applying a structured cognitive learning approach to
instruction tuning in contemporary large language models like ChatGPT and
GPT-4. Unlike the previous conventional randomized instruction dataset, we
propose a highly structured synthetic dataset that mimics the progressive and
organized nature of human education. We curate our dataset by aligning it with
educational frameworks, incorporating meta information including its topic and
cognitive rigor level for each sample. Our dataset covers comprehensive
fine-grained topics spanning diverse educational stages (from middle school to
graduate school) with various questions for each topic to enhance conceptual
depth using Bloom's taxonomy-a classification framework distinguishing various
levels of human cognition for each concept. The results demonstrate that this
cognitive rigorous training approach yields significant performance
enhancements - +3.06 on the MMLU benchmark and an additional +1.28 on AI2
Reasoning Challenge (hard set) - compared to conventional randomized training,
all while avoiding additional computational costs. This research highlights the
potential of leveraging human learning principles to enhance the capabilities
of language models in comprehending and responding to complex instructions and
tasks.

应用结构化认知学习方法于现代大型语言模型的指令调优，通过提供高度结构化合成数据集，模仿人类教育的渐进和有组织性，可以显著提升语言模型在理解和响应复杂指令和任务方面的能力。