Summary of Dynamath: a Dynamic Visual Benchmark For Evaluating Mathematical Reasoning Robustness Of Vision Language Models, by Chengke Zou et al.
DynaMath: A Dynamic Visual Benchmark for Evaluating Mathematical Reasoning Robustness of Vision Language Models
by Chengke Zou, Xingang Guo, Rui Yang, Junyu Zhang, Bin Hu, Huan Zhang
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 investigates the limitations of Vision-Language Models (VLMs) in mathematical reasoning tasks that involve visual context. While SOTA VLMs like GPT-4o can excel in many scenarios, they consistently fail when required to apply solution steps to similar problems with minor modifications. The authors introduce DynaMath, a dynamic visual math benchmark designed to assess the robustness of VLMs’ mathematical reasoning capabilities. DynaMath includes 501 high-quality seed questions, each represented as a Python program, which can be used to generate a large set of concrete questions with varying input conditions. The authors evaluate 14 SOTA VLMs using 5,010 generated concrete questions and find that the worst-case model accuracy is significantly lower than the average-case accuracy. This study highlights the need to develop more reliable models for mathematical reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well machines can do math problems when they involve pictures or diagrams. Right now, these machine models are really good at solving certain types of math problems, but they struggle when the problem is similar but with a small change. The researchers created a new way to test these models by generating many different versions of each math problem. They used this system to evaluate 14 of the best machine models and found that even the best ones have trouble solving math problems when things are changed just a little bit. This study shows that machines need to get better at doing math if they’re going to be really useful. |
Keywords
» Artificial intelligence » Gpt
