Summary of Polymath: a Challenging Multi-modal Mathematical Reasoning Benchmark, by Himanshu Gupta and Shreyas Verma and Ujjwala Anantheswaran and Kevin Scaria and Mihir Parmar and Swaroop Mishra and Chitta Baral
Polymath: A Challenging Multi-modal Mathematical Reasoning Benchmark
by Himanshu Gupta, Shreyas Verma, Ujjwala Anantheswaran, Kevin Scaria, Mihir Parmar, Swaroop Mishra, Chitta Baral
First submitted to arxiv on: 6 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 paper presents PolyMATH, a challenging benchmark evaluating the general cognitive reasoning abilities of Multi-modal Large Language Models (MLLMs). It comprises 5,000 high-quality images across 10 categories, including pattern recognition and spatial reasoning. The authors conducted a comprehensive evaluation using four prompting strategies, revealing that the best scores achieved by Claude-3.5 Sonnet, GPT-4o, and Gemini-1.5 Pro are ~41%, ~36%, and ~27% respectively. A fine-grained error analysis shows that MLLMs struggle with spatial relations and high-level reasoning, while an ablation study demonstrates that models don’t truly comprehend visual diagrams. The results highlight the room for improvement in multi-modal reasoning and provide insights to guide future MLLM development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a special test called PolyMATH to see how well computers can understand pictures and make smart decisions. It uses 5,000 images that are tricky to understand and asks 15 different computer models to solve the problems. The best computer did okay, but not perfectly. When we looked closer at what they got wrong, we found out that these computers have trouble understanding things that need spatial thinking and making long-term plans. We also tried giving them descriptions instead of pictures and saw that they do better when given words. This tells us that the computers are not really good at understanding pictures yet. |
Keywords
» Artificial intelligence » Claude » Gemini » Gpt » Multi modal » Pattern recognition » Prompting
