Summary of Mathglm-vision: Solving Mathematical Problems with Multi-modal Large Language Model, by Zhen Yang et al.
MathGLM-Vision: Solving Mathematical Problems with Multi-Modal Large Language Model
by Zhen Yang, Jinhao Chen, Zhengxiao Du, Wenmeng Yu, Weihan Wang, Wenyi Hong, Zhihuan Jiang, Bin Xu, Jie Tang
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 Large language models (LLMs) have shown impressive capabilities in mathematical reasoning for text-based problems. However, multi-modal LLMs specialized in mathematics tend to focus on geometric problems, neglecting diverse visual information in other areas. Current datasets are limited in diversity and complexity. To address this, we developed a fine-tuning dataset, MathVL, and created series of specialized MLLMs, MathGLM-Vision, through Supervised Fine-Tuning (SFT) on MathVL with various parameter-scale backbones. We evaluated MathGLM-Vision’s effectiveness on public benchmarks and our curated test set, consisting of 2,000 problems. Results show significant improvements compared to existing models, including backbone models and open-source MLLMs. This highlights the importance of diverse datasets in enhancing mathematical reasoning abilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to teach a super smart computer how to solve math problems that involve pictures. That’s what researchers did by creating a new kind of AI model called MathGLM-Vision. They also made a special dataset, MathVL, with 2,000 math problems that include pictures. The goal was to make the AI better at solving these kinds of math problems. To test it, they compared their model to other models and found that MathGLM-Vision did much better. This shows how important it is to have a wide variety of math problems for AI to learn from. |
Keywords
» Artificial intelligence » Fine tuning » Multi modal » Supervised
