Large Language Models (LLMs) have not only exhibited exceptional performance
across various tasks, but also demonstrated sparks of intelligence. Recent
studies have focused on assessing their capabilities on human exams and
revealed their impressive competence in different domains. However, cognitive
research on the overall knowledge structure of LLMs is still lacking. In this
paper, based on educational diagnostic assessment method, we conduct an
evaluation using MoocRadar, a meticulously annotated human test dataset based
on Bloom Taxonomy. We aim to reveal the knowledge structures of LLMs and gain
insights of their cognitive capabilities. This research emphasizes the
significance of investigating LLMs' knowledge and understanding the disparate
cognitive patterns of LLMs. By shedding light on models' knowledge, researchers
can advance development and utilization of LLMs in a more informed and
effective manner.

通过使用教育诊断评估方法，本研究在 MoocRadar 上进行评估，这是一个基于布鲁姆分类法的人工测试数据集，旨在揭示大型语言模型的知识结构并了解其认知能力的差异模式，从而为研究人员在 LLMs 的知识方面提供更明确、更有效的发展和利用。