Transparent AI for Mathematics: Transformer-Based Large Language Models for Mathematical Entity Relationship Extraction with XAI

TL;DR

This paper introduces a transformer-based approach for extracting relationships between mathematical entities from text, incorporating explainability to improve transparency and trust in the model's predictions.

Contribution

It formulates mathematical understanding as a relation extraction task, applies BERT for high accuracy, and integrates SHAP for explainability, advancing interpretable AI in mathematical text analysis.

Findings

BERT achieved 99.39% accuracy in relation extraction.

Explainability analysis highlights key textual features influencing predictions.

The framework supports automated problem solving and educational tools.

Abstract

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.