Explain with Visual Keypoints Like a Real Mentor! A Benchmark for Multimodal Solution Explanation

• URL: http://arxiv.org/abs/2504.03197v3
• Date: Sun, 06 Jul 2025 10:36:13 GMT
• Title: Explain with Visual Keypoints Like a Real Mentor! A Benchmark for Multimodal Solution Explanation
• Authors: Jaewoo Park, Jungyang Park, Dongju Jang, Jiwan Chung, Byungwoo Yoo, Jaewoo Shin, Seonjoon Park, Taehyeong Kim, Youngjae Yu,
• Abstract summary: We introduce the multimodal solution explanation task, designed to evaluate whether models can identify visual keypoints, such as auxiliary lines, points, angles, and generate explanations that incorporate these key elements essential for understanding.<n>Our empirical results show that, aside from recent large-scale open-source and closed-source models, most generalist open-source models, and even math-specialist models, struggle with the multimodal solution explanation task.<n>This highlights a significant gap in current LLMs' ability to reason and explain with visual grounding in educational contexts.
• Score: 19.4261670152456
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the rapid advancement of mathematical reasoning capabilities in Large Language Models (LLMs), AI systems are increasingly being adopted in educational settings to support students' comprehension of problem-solving processes. However, a critical component remains underexplored in current LLM-generated explanations: multimodal explanation. In real-world instructional contexts, human tutors routinely employ visual aids, such as diagrams, markings, and highlights, to enhance conceptual clarity. To bridge this gap, we introduce the multimodal solution explanation task, designed to evaluate whether models can identify visual keypoints, such as auxiliary lines, points, angles, and generate explanations that incorporate these key elements essential for understanding. To evaluate model performance on this task, we propose ME2, a multimodal benchmark consisting of 1,000 math problems annotated with visual keypoints and corresponding explanatory text that references those elements. Our empirical results show that, aside from recent large-scale open-source and closed-source models, most generalist open-source models, and even math-specialist models, struggle with the multimodal solution explanation task. This highlights a significant gap in current LLMs' ability to reason and explain with visual grounding in educational contexts. We expect that the multimodal solution explanation task and the ME2 dataset will catalyze further research on LLMs in education and promote their use as effective, explanation-oriented AI tutors.

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