Knowledge tagging for questions is vital in modern intelligent educational applications, including learning progress diagnosis, practice question recommendations, and course content organization. Traditionally, these annotations have been performed by pedagogical experts, as the task demands not only a deep semantic understanding of question stems and knowledge definitions but also a strong ability to link problem-solving logic with relevant knowledge concepts. With the advent of advanced natural language processing (NLP) algorithms, such as pre-trained language models and large language models (LLMs), pioneering studies have explored automating the knowledge tagging process using various machine learning models. In this paper, we investigate the use of a multi-agent system to address the limitations of previous algorithms, particularly in handling complex cases involving intricate knowledge definitions and strict numerical constraints. By demonstrating its superior performance on the publicly available math question knowledge tagging dataset, MathKnowCT, we highlight the significant potential of an LLM-based multi-agent system in overcoming the challenges that previous methods have encountered. Finally, through an in-depth discussion of the implications of automating knowledge tagging, we underscore the promising results of deploying LLM-based algorithms in educational contexts.

翻译：问题知识标注在现代智能教育应用中至关重要，包括学习进度诊断、习题推荐和课程内容组织。传统上，这些标注由教学专家完成，因为该任务不仅需要对题目题干和知识定义有深入的语义理解，还需要将解题逻辑与相关知识概念进行关联的强能力。随着先进自然语言处理（NLP）算法（如预训练语言模型和大语言模型（LLMs））的出现，开创性研究已探索使用各种机器学习模型自动化知识标注过程。在本文中，我们研究利用多智能体系统来解决先前算法的局限性，特别是在处理涉及复杂知识定义和严格数值约束的复杂案例方面。通过在公开可用的数学问题知识标注数据集MathKnowCT上展示其优越性能，我们强调了基于LLM的多智能体系统在克服先前方法所遇挑战方面的巨大潜力。最后，通过对自动化知识标注影响的深入讨论，我们强调了在教育场景中部署基于LLM算法的前景广阔的结果。