MMSciBench: Benchmarking Language Models on Multimodal Scientific Problems

• URL: http://arxiv.org/abs/2503.01891v1
• Date: Thu, 27 Feb 2025 15:38:43 GMT
• Title: MMSciBench: Benchmarking Language Models on Multimodal Scientific Problems
• Authors: Xinwu Ye, Chengfan Li, Siming Chen, Xiangru Tang, Wei Wei,
• Abstract summary: We present MMSciBench, a benchmark for evaluating mathematical and physical reasoning through text-only and text-image formats.<n>Our analysis exposes critical gaps in complex reasoning and visual-textual integration, establishing MMSciBench as a rigorous standard.
• Score: 12.931916288612483
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in large language models (LLMs) and vision-language models (LVLMs) have shown promise across many tasks, yet their scientific reasoning capabilities remain untested, particularly in multimodal settings. We present MMSciBench, a benchmark for evaluating mathematical and physical reasoning through text-only and text-image formats, with human-annotated difficulty levels, solutions with detailed explanations, and taxonomic mappings. Evaluation of state-of-the-art models reveals significant limitations, with even the best model achieving only \textbf{63.77\%} accuracy and particularly struggling with visual reasoning tasks. Our analysis exposes critical gaps in complex reasoning and visual-textual integration, establishing MMSciBench as a rigorous standard for measuring progress in multimodal scientific understanding. The code for MMSciBench is open-sourced at GitHub, and the dataset is available at Hugging Face.

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