Summary of Fuse, Reason and Verify: Geometry Problem Solving with Parsed Clauses From Diagram, by Ming-liang Zhang et al.
Fuse, Reason and Verify: Geometry Problem Solving with Parsed Clauses from Diagram
by Ming-Liang Zhang, Zhong-Zhi Li, Fei Yin, Liang Lin, Cheng-Lin Liu
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 A novel neural-symbolic model for plane geometry problem solving (PGPS), named PGPSNet-v2, is proposed to tackle the challenges of multi-modal understanding, multi-hop reasoning, and theorem knowledge application. The model consists of three key steps: modal fusion, reasoning process, and knowledge verification. Modal fusion leverages textual clauses to express fine-grained structural and semantic content of geometry diagrams, fusing diagram with textual problem through structural-semantic pre-training. Reasoning is achieved through an explicable solution program that describes the geometric reasoning process, employing a self-limited decoder to generate solutions autoregressively. To reduce solution errors, a multi-level theorem verifier eliminates solutions that do not match geometric principles, alleviating neural model hallucination. The model is evaluated on datasets Geometry3K and PGPS9K, outperforming existing symbolic and neural solvers in GPS performance while maintaining good explainability and reliability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PGPSNet-v2 is a new way to solve geometry problems using AI. This system uses text and diagrams together to understand and reason about geometry problems. It’s like having a math tutor that can explain its thinking. The model has three main parts: combining text and diagrams, figuring out the solution, and checking if it makes sense. PGPS9K is a big dataset of geometry problems with detailed answers. Experiments show that this system works better than others for solving geometry problems. |
Keywords
» Artificial intelligence » Decoder » Hallucination » Multi modal
