Introduction to GeoX
GeoX is a multi-modal large model designed for automatic geometric problem solving, utilizing three progressive training stages to enhance diagram understanding and reasoning. In this paper, we validate that the formal vision-language training paradigm is a simple-yet-effective solution for complex mathematical diagram learning.
Data Preparation for GeoX
Step 1. Data for Unimodal Pre-training
Additionally, we use existing geometric text to build a corpus, which is detailed in
our paper.
To train the GS-Former, please prepare the
unified formal annotations and paired
images.
We use the GeoQA, UniGeo, Geometry3K, and PGPS9K datasets for fine-tuning and evaluation:
Note: Due to copyright restrictions, we are currently only providing links for these datasets. Full datasets for tuning and evaluation organized by us will be provided via email. If you need it, please contact us by
email.
For more details, please refer to
our paper and
GitHub repository. If you find our work helpful, please consider starring ⭐ in this repository and citing us:
1@article{xia2024geox,
2 title={GeoX: Geometric Problem Solving Through Unified Formalized Vision-Language Pre-training},
3 author={Xia, Renqiu and Li, Mingsheng and Ye, Hancheng and Wu, Wenjie and Zhou, Hongbin and Yuan, Jiakang and Peng, Tianshuo and Cai, Xinyu and Yan, Xiangchao and Wang, Bin and others},
4 journal={arXiv preprint arXiv:2412.11863},
5 year={2024}
6}
