Summary of Seeing the Forest and the Trees: Solving Visual Graph and Tree Based Data Structure Problems Using Large Multimodal Models, by Sebastian Gutierrez et al.
Seeing the Forest and the Trees: Solving Visual Graph and Tree Based Data Structure Problems using Large Multimodal Models
by Sebastian Gutierrez, Irene Hou, Jihye Lee, Kenneth Angelikas, Owen Man, Sophia Mettille, James Prather, Paul Denny, Stephen MacNeil
First submitted to arxiv on: 15 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY)
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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 Recent advancements in generative AI systems have raised concerns about academic integrity among educators. This paper investigates the capabilities of large multimodal models (LMMs) to solve graph and tree data structure problems based only on images. The authors computationally construct and evaluate a novel benchmark dataset comprising 9,072 samples of diverse graph and tree data structure tasks to assess the performance of six model families: GPT-4o, GPT-4v, Gemini 1.5 Pro, Gemini 1.5 Flash, Gemini 1.0 Pro Vision, and Claude 3. The results show that GPT-4o performed best on trees with an accuracy of 87.6%, while Gemini 1.5 Flash achieved the highest accuracy on graph samples at 56.2%. These findings highlight the influence of structural and visual variations on model performance, introducing a new LMM benchmark for facilitating replication and further exploration. The potential implications for pedagogy and assessment practices are significant. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how AI models can solve complex computer problems just by looking at pictures. Right now, some AI models can do tasks that usually require text-based input. But what happens when we show these models images of code blocks instead? The researchers created a special test dataset with lots of different coding problems to see which AI models are best at solving them based only on the image. They found that one model, GPT-4o, is really good at solving tree-shaped problems, while another model, Gemini 1.5 Flash, is better at graph-shaped problems. This study shows how important it is to consider how different images can affect AI models’ performance and could have big implications for teaching and testing computer skills. |
Keywords
» Artificial intelligence » Claude » Gemini » Gpt
