Summary of Evaluating Large Vision-and-language Models on Children’s Mathematical Olympiads, by Anoop Cherian et al.
Evaluating Large Vision-and-Language Models on Children’s Mathematical Olympiads
by Anoop Cherian, Kuan-Chuan Peng, Suhas Lohit, Joanna Matthiesen, Kevin Smith, Joshua B. Tenenbaum
First submitted to arxiv on: 22 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 Large vision and language models (LVLMs) have recently made significant progress in general-purpose problem solving abilities, outperforming humans in various tasks requiring higher-order cognitive skills. However, it is unclear whether these AI models can truly generalize problem-solving abilities like humans do. This paper aims to fill this knowledge gap by evaluating state-of-the-art LVLMs on mathematical and algorithmic reasoning using visuo-linguistic problems from children’s Olympiads. The study uses a dataset of 840 problems from the Mathematical Kangaroo (MK) Olympiad, designed for children aged 1-12, to analyze LVLMs’ power on mathematical reasoning. Results show that modern LVLMs demonstrate increasingly powerful reasoning skills in solving higher-grade problems but struggle with puzzles designed for younger children. The study highlights a lack of significant correlation between AI models and young children’s reasoning capabilities, suggesting distinct types of reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large vision and language models have made big progress in solving problems like humans do. But can they really solve problems in the same way as people? This paper tries to answer this question by testing how well these AI models can reason mathematically using puzzles from a kids’ competition called Mathematical Kangaroo Olympiad. They used 840 problems designed for kids aged 1-12 and found that modern AI models are good at solving harder math problems but struggle with easier ones meant for younger kids. The study shows that the way AI models reason is different from how kids learn math and logic. |
