Summary of R-cot: Reverse Chain-of-thought Problem Generation For Geometric Reasoning in Large Multimodal Models, by Linger Deng et al.
R-CoT: Reverse Chain-of-Thought Problem Generation for Geometric Reasoning in Large Multimodal Models
by Linger Deng, Yuliang Liu, Bohan Li, Dongliang Luo, Liang Wu, Chengquan Zhang, Pengyuan Lyu, Ziyang Zhang, Gang Zhang, Errui Ding, Yingying Zhu, Xiang Bai
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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 This paper proposes a novel approach to generating high-quality data for Large Multimodal Models (LMMs) that struggle with mathematical geometric reasoning. The authors introduce the Reverse Chain-of-Thought (R-CoT) geometry problem generation pipeline, which consists of two stages: GeoChain and Reverse A&Q. GeoChain produces high-fidelity geometric images and corresponding descriptions highlighting relations among geometric elements, while Reverse A&Q reasons step-by-step based on the descriptions and generates questions in reverse from the reasoning results. The proposed method is evaluated on multiple LMM baselines and achieves significant improvements, outperforming previous state-of-the-art models by a substantial margin. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a way to make computers better at understanding math problems. Right now, these machines struggle with math that involves pictures. To fix this, the researchers came up with a new process for creating math problems and their answers. This process has two parts: making pictures of geometric shapes and then asking questions about those shapes based on the answers. The results show that this approach makes computers much better at solving math problems than they were before. |
