Knowledge concept tagging for questions plays a crucial role in contemporary
intelligent educational applications, including learning progress diagnosis,
practice question recommendations, and course content organization.
Traditionally, these annotations have been conducted manually with help from
pedagogical experts, as the task requires not only a strong semantic
understanding of both question stems and knowledge definitions but also deep
insights into connecting question-solving logic with corresponding knowledge
concepts. In this paper, we explore automating the tagging task using Large
Language Models (LLMs), in response to the inability of prior manual methods to
meet the rapidly growing demand for concept tagging in questions posed by
advanced educational applications. Moreover, the zero/few-shot learning
capability of LLMs makes them well-suited for application in educational
scenarios, which often face challenges in collecting large-scale,
expertise-annotated datasets. By conducting extensive experiments with a
variety of representative LLMs, we demonstrate that LLMs are a promising tool
for concept tagging in math questions. Furthermore, through case studies
examining the results from different LLMs, we draw some empirical conclusions
about the key factors for success in applying LLMs to the automatic concept
tagging task.

基于大语言模型的知识概念标记在数学问题的自动标记任务中显示出潜力，由于其零 / 少样本学习能力，适用于教育场景中收集大规模、专业标记数据集所面临的挑战。