New benchmark reveals LLMs struggle with mathematical diagram generation

Researchers have introduced Math-Vision Diagrams, a new benchmark designed to evaluate the capabilities of Large Language Models (LLMs) in generating mathematical diagrams from textual prompts. This benchmark is the first to unify text-to-code and text-to-image generation paradigms for mathematical diagrams. It comprises 2920 image-prompt pairs selected from competition problems and utilizes a pipeline involving LLMs and subject-matter expert curation, along with a suite of evaluation metrics. Initial testing reveals that current LLMs struggle significantly with this task. AI

IMPACT This benchmark will drive improvements in LLMs' ability to generate complex mathematical visualizations, crucial for educational and scientific applications.

RANK_REASON The cluster describes a new academic benchmark for evaluating LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• DiagramGenBenchmark
• Hugging Face
• MathVision
• Math-Vision Diagrams
• MathVista
• MermaidSeqBench

• paper
• product

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