Summary of Learning to Solve Geometry Problems Via Simulating Human Dual-reasoning Process, by Tong Xiao et al.
Learning to Solve Geometry Problems via Simulating Human Dual-Reasoning Process
by Tong Xiao, Jiayu Liu, Zhenya Huang, Jinze Wu, Jing Sha, Shijin Wang, Enhong Chen
First submitted to arxiv on: 10 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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 presents a novel approach to solving Geometry Problem Solving (GPS) tasks by mimicking human cognition. The proposed Dual-Reasoning Geometry Solver (DualGeoSolver) combines implicit and explicit reasoning processes, inspired by dual-process theory, to tackle GPS challenges. DualGeoSolver consists of two systems: the Knowledge System, which provides diagram information and geometry knowledge, and the Inference System, which specifies goals and applies knowledge to generate program tokens for resolving them. The system iteratively updates its reasoning process, behaving more similarly to human cognition. Experimental results on GeoQA and GeoQA+ datasets show that DualGeoSolver outperforms existing methods in both solving accuracy and robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to solve math problems called Geometry Problem Solving (GPS). GPS requires people to understand diagrams and apply geometry rules to solve problems. Current AI systems aren’t good at this because they only focus on understanding the diagram, not how humans actually think. The researchers created a system that works like a human would: it has two parts, one for understanding and one for applying what it knows. They tested this system on math problems and it did much better than other AI systems. |
Keywords
* Artificial intelligence * Inference
