GeoGPT4V: Towards Geometric Multi-modal Large Language Models with Geometric Image Generation

Abstract

Large language models have seen widespread adoption in math problem-solving. However, in geometry problems that usually require visual aids for better understanding, even the most advanced multi-modal models currently still face challenges in effectively using image information. High-quality data is crucial for enhancing the geometric capabilities of multi-modal models, yet existing open-source datasets and related efforts are either too challenging for direct model learning or suffer from misalignment between text and images. To overcome this issue, we introduce a novel pipeline that leverages GPT-4 and GPT-4V to generate relatively basic geometry problems with aligned text and images, facilitating model learning. We have produced a dataset of 4.9K geometry problems and combined it with 19K open-source data to form our GeoGPT4V dataset. Experimental results demonstrate that the GeoGPT4V dataset significantly improves the geometry performance of various models on the MathVista and MathVision benchmarks. The code is available at https://github.com/Lanyu0303/GeoGPT4V_Project.

@inproceedings{cai2024geogpt4v,
  title={GeoGPT4V: Towards Geometric Multi-modal Large Language Models with Geometric Image Generation},
  author={Cai, Shihao and Bao, Keqin and Guo, Hangyu and Zhang, Jizhi and Song, Jun and Zheng, Bo},
  booktitle={EMNLP},
  year={2024},
}

Shihao Cai

蔡仕豪

Keqin Bao

鲍克勤

Jizhi Zhang

张及之