Summary of Measuring Multimodal Mathematical Reasoning with Math-vision Dataset, by Ke Wang et al.
Measuring Multimodal Mathematical Reasoning with MATH-Vision Dataset
by Ke Wang, Junting Pan, Weikang Shi, Zimu Lu, Mingjie Zhan, Hongsheng Li
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); History and Overview (math.HO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents the MATH-Vision (MATH-V) dataset, a comprehensive collection of 3,040 high-quality mathematical problems with visual contexts sourced from real math competitions. The dataset spans 16 distinct mathematical disciplines and is graded across five levels of difficulty, providing a diverse set of challenges for evaluating the mathematical reasoning abilities of Large Multimodal Models (LMMs). The authors note that current LMMs still struggle to match human-level performance on existing benchmarks like MathVista, despite approaching human-level performance. Through experimentation, they identify a significant performance gap between current LMMs and humans on MATH-V, highlighting the need for further advancements in LMMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new dataset called MATH-Vision (MATH-V) that has lots of math problems with pictures. The problems are from real math competitions and cover many different types of math. There are 3,040 problems, and they’re all hard or easy, so you can see how well computers do on each kind of problem. Right now, computers aren’t as good at math as humans are, even though they’re getting better. The people who made this dataset want to help computers get better by giving them more challenges. |
