Summary of Geo-llava: a Large Multi-modal Model For Solving Geometry Math Problems with Meta In-context Learning, by Shihao Xu et al.
Geo-LLaVA: A Large Multi-Modal Model for Solving Geometry Math Problems with Meta In-Context Learning
by Shihao Xu, Yiyang Luo, Wei Shi
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computational Geometry (cs.CG)
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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 approach is proposed to tackle the challenges of large language models (LLMs) in solving geometry mathematics problems. The current methods rely on symbolic character awareness, but a more comprehensive framework is needed to address these problems. To this end, a dataset called GeoMath is collected from Chinese high school education websites, containing solid geometry questions and answers with accurate reasoning steps. A Large Multi-modal Model (LMM) framework, named Geo-LLaVA, is proposed, which incorporates retrieval augmentation with supervised fine-tuning in the training stage and employs in-context learning during inference to improve performance. The fine-tuned model achieves state-of-the-art performance on selected questions of the GeoQA dataset and GeoMath dataset. Moreover, the model demonstrates the ability to solve solid geometry problems and generate reasonable picture descriptions and problem-solving steps. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way for computers to solve math problems that involve shapes and spatial reasoning. Right now, computers can only understand simple math problems by looking at symbols like numbers and letters. But this new approach allows computers to understand more complex math problems that involve visual elements and spatial reasoning. The team created a special dataset of geometry math questions and answers to help train the computer model. They also developed a new framework called Geo-LLaVA, which uses a combination of techniques to improve the computer’s performance. With this new approach, computers can now solve solid geometry problems and generate reasonable picture descriptions and problem-solving steps. |
Keywords
» Artificial intelligence » Fine tuning » Inference » Multi modal » Supervised
