Diagram-MMU: A Multi-Modal Benchmark for Scientific Diagrams
Researchers introduce Diagram-MMU, a benchmark containing 3.7k scientific diagrams and 18.3k validated questions to evaluate multimodal LLMs on diagram parsing.
- Diagram-MMU introduces a large, validated dataset for testing multimodal LLMs on scientific diagram tasks.
- The benchmark covers six domains and three tasks, offering a comprehensive evaluation framework.
- Initial results indicate existing MLLMs have notable weaknesses in diagram understanding and code generation.
The rapid growth of multimodal large language models has opened new possibilities for scientific writing and collaboration, exemplified by tools like OpenAI Prism that can turn diagrams into LaTeX TikZ code. To systematically assess these models' ability to understand and generate scientific diagrams, a team of researchers released Diagram-MMU, a benchmark composed of 3.7 thousand curated diagrams spanning six scientific domains.
Diagram-MMU includes 18.3 thousand human‑validated questions that probe three core tasks: diagram parsing, semantic understanding, and code generation. The benchmark is designed to be a standard testbed for evaluating how well MLLMs can translate visual information into precise, editable LaTeX representations.
Early experiments show that current state‑of‑the‑art MLLMs still struggle with many of the benchmark's challenges, highlighting gaps in visual reasoning and domain‑specific knowledge. The authors hope that the dataset will drive future research toward more robust multimodal reasoning and better integration of visual and textual modalities.
By providing a publicly available, rigorously validated resource, Diagram-MMU aims to accelerate progress in scientific AI tools and set a baseline for future model comparisons.
Provides a concrete test suite for building and improving multimodal model capabilities.
Offers a research resource for studying visual‑language integration in AI.
Shows the growing need for AI to handle complex scientific visuals.
- MLLM
- Multimodal Large Language Model that processes both text and visual inputs.
- TikZ
- A LaTeX package for creating vector graphics, often used for scientific diagrams.
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