Summary of Turtlebench: a Visual Programming Benchmark in Turtle Geometry, by Sina Rismanchian et al.
TurtleBench: A Visual Programming Benchmark in Turtle Geometry
by Sina Rismanchian, Yasaman Razeghi, Sameer Singh, Shayan Doroudi
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 The abstract presents a benchmark called TurtleBench to evaluate the ability of large multimodal models (LMMs) to interpret geometric patterns in images and scenes. The benchmark is designed to assess LMMs’ capacity to generate precise code outputs given visual examples, textual instructions, or both. Inspired by turtle geometry, a concept used to teach children coding and geometric concepts, TurtleBench features tasks with patterned shapes that have underlying algorithmic logic. The evaluation reveals that leading LMMs struggle significantly with these tasks, highlighting the gap between human and AI performance in intuitive and visual geometrical understanding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TurtleBench is a new way to test how well artificial intelligence (AI) models can understand geometric patterns in images. Right now, AI models are really good at doing things like recognizing objects in pictures, but they’re not very good at understanding the rules behind those patterns. TurtleBench tries to fix this by giving AI models simple instructions and asking them to create code that follows those rules. The results show that even the best AI models aren’t very good at this yet. |
