Summary of We-math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?, by Runqi Qiao et al.
We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?
by Runqi Qiao, Qiuna Tan, Guanting Dong, Minhui Wu, Chong Sun, Xiaoshuai Song, Zhuoma GongQue, Shanglin Lei, Zhe Wei, Miaoxuan Zhang, Runfeng Qiao, Yifan Zhang, Xiao Zong, Yida Xu, Muxi Diao, Zhimin Bao, Chen Li, Honggang Zhang
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Symbolic Computation (cs.SC)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 introduces WE-MATH, a benchmark designed to assess the problem-solving principles in visual mathematical reasoning beyond end-to-end performance. Existing benchmarks, such as MathVista and MathVerse, focus on result-oriented performance but neglect underlying knowledge acquisition and generalization principles. The authors collect and categorize 6.5K visual math problems spanning 67 hierarchical knowledge concepts and five layers of granularity. They decompose composite problems into sub-problems according to required knowledge concepts and introduce a novel four-dimensional metric (IK, IG, CM, and RM) to hierarchically assess inherent issues in Large Multimodal Models’ reasoning process. The authors evaluate existing LMMs and reveal a negative correlation between solving steps and problem-specific performance. They also confirm the Insufficient Knowledge issue can be improved via knowledge augmentation strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new benchmark for visual mathematical reasoning that looks at how models solve problems rather than just the answers they give. It’s like a puzzle where you need to show your work, not just get the right answer. The authors test many language models and find that most of them are good at solving simple math problems but struggle with harder ones that require more thinking. |
Keywords
* Artificial intelligence * Generalization
