Summary of Tangram: Benchmark For Evaluating Geometric Element Recognition in Large Multimodal Models, by Chao Zhang and Jiamin Tang and Jing Xiao
Tangram: Benchmark for Evaluating Geometric Element Recognition in Large Multimodal Models
by Chao Zhang, Jiamin Tang, Jing Xiao
First submitted to arxiv on: 25 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduces Tangram, a novel benchmark designed to evaluate Large Multimodal Models (LMMs) on geometric element recognition. Tangram comprises 1,080 diverse geometric diagrams paired with four questions each, resulting in 4,320 visual-question-answer pairs. Unlike existing benchmarks that emphasize higher-level cognition and reasoning, Tangram focuses on understanding basic geometric elements, requiring models to perform a simple yet challenging counting task. The paper systematically evaluates 13 prominent LMMs, including GPT-4o and Claude 3.5 Sonnet, revealing significant challenges even in seemingly straightforward tasks. Despite the top-performing model achieving an accuracy of only 53.0%, the findings underscore the limitations of current multimodal AI systems in handling basic perception tasks, inspiring the development of the next generation of expert-level multimodal foundational models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new test called Tangram to see how good artificial intelligence (AI) models are at recognizing shapes and simple math problems. They used 1,080 different geometric diagrams from school exams and textbooks, with four questions each. The AI models struggled even with simple tasks, showing that they still have a lot to learn about understanding basic shapes and math. The best-performing AI model got only 53% of the answers correct, which is much lower than what humans can do. This shows that we need better AI models that can understand simple things before we can make more advanced ones. |
Keywords
» Artificial intelligence » Claude » Gpt
