探索大型语言模型的认知知识结构：一种教育诊断评估方法
Exploring the Cognitive Knowledge Structure of Large Language Models: An Educational Diagnostic Assessment Approach
AI总结：
本文基于教育诊断评估方法，利用基于Bloom分类法标注的MoocRadar数据集评估大型语言模型，揭示其知识结构与认知能力，强调理解模型认知模式对推进其开发与利用的重要性。
摘要：
大型语言模型（LLMs）不仅在各种任务中表现出色，还展现出智能的火花。近期研究关注评估其在人类考试中的能力，揭示了它们在不同领域的出色表现。然而，对LLMs整体知识结构的认知研究仍然缺乏。本文基于教育诊断评估方法，使用MoocRadar进行了评估，这是一个基于Bloom Taxonomy精心标注的人类测试数据集。我们旨在揭示LLMs的知识结构并深入了解其认知能力。本研究强调调查LLMs知识及理解其不同认知模式的重要性。通过阐明模型的知识，研究人员能以更知情和有效的方式推进LLMs的开发与利用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
