Summary of Visaidmath: Benchmarking Visual-aided Mathematical Reasoning, by Jingkun Ma et al.
VisAidMath: Benchmarking Visual-Aided Mathematical Reasoning
by Jingkun Ma, Runzhe Zhan, Derek F. Wong, Yang Li, Di Sun, Hou Pong Chan, Lidia S. Chao
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 A novel benchmark, VisAidMath, is proposed to evaluate the problem-solving abilities of large language models (LLMs) and large multi-modal models (LMMs) within visual contexts. The benchmark includes 1,200 challenging mathematical problems from various branches, with vision-aid formulations and difficulty levels. Comprehensive evaluations on ten mainstream LLMs and LMMs reveal deficiencies in their visual-aided reasoning process, particularly hallucination regarding implicit visual reasoning. These findings highlight the importance of improving the visual-aided problem-solving abilities of these models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are getting better at solving math problems! But how do they use visual information to help them solve these problems? Researchers created a special test called VisAidMath to find out. They gave 10 popular LLMs and LMMs lots of tricky math questions with pictures or diagrams, and then measured how well they did. The results showed that some LLMs are really good at using visual information, but others struggle. This means we need to improve the way these models think about visual problems to help them get better. |
Keywords
» Artificial intelligence » Hallucination » Multi modal
