透明AI用于数学：基于Transformer的大语言模型用于数学实体关系提取与XAI
Transparent AI for Mathematics: Transformer-Based Large Language Models for Mathematical Entity Relationship Extraction with XAI
AI总结：
本文提出基于Transformer的大语言模型，结合XAI方法，实现数学实体关系提取，达到高准确率并提升模型可解释性，为自动问题解决和知识图谱构建提供有效框架。
摘要：
数学文本理解是一项具有挑战性的任务，因为存在专门的实体和它们之间的复杂关系。本研究将数学问题解释制定为数学实体关系提取（MERE）任务，其中操作数被视为实体，运算符被视为其关系。基于Transformer的模型被应用于自动从数学文本中提取这些关系，双向编码表示从Transformer（BERT）在最佳性能上达到99.39%的准确率。为了增强模型预测的透明性和可信度，使用可解释人工智能（XAI）的Shapley加性解释（SHAP）进行整合。可解释性分析揭示了特定文本和数学特征如何影响关系预测，提供了关于特征重要性和模型行为的见解。通过结合基于Transformer的学习、任务特定的数据集和可解释性建模，本工作提供了一个有效且可解释的MERE框架，支持未来在自动化问题解决、知识图谱构建和智能教育系统中的应用。
英文摘要：
Mathematical text understanding is a challenging task due to the presence of specialized entities and complex relationships between them. This study formulates mathematical problem interpretation as a Mathematical Entity Relation Extraction (MERE) task, where operands are treated as entities and operators as their relationships. Transformer-based models are applied to automatically extract these relations from mathematical text, with Bidirectional Encoder Representations from Transformers (BERT) achieving the best performance, reaching an accuracy of 99.39%. To enhance transparency and trust in the model's predictions, Explainable Artificial Intelligence (XAI) is incorporated using Shapley Additive Explanations (SHAP). The explainability analysis reveals how specific textual and mathematical features influence relation prediction, providing insights into feature importance and model behavior. By combining transformer-based learning, a task-specific dataset, and explainable modeling, this work offers an effective and interpretable framework for MERE, supporting future applications in automated problem solving, knowledge graph construction, and intelligent educational systems.
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。
