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

TL;DR

This paper introduces GeoGPT4V, a new dataset and pipeline for training multi-modal large language models in geometry, significantly improving their ability to understand and generate geometric visual information.

Contribution

The paper presents a novel data generation pipeline using GPT-4 and GPT-4V, creating a high-quality, aligned geometry problem dataset to enhance multi-modal model performance.

Findings

GeoGPT4V dataset improves geometry understanding on benchmarks

Generated data enhances model performance significantly

Pipeline enables effective multi-modal learning in geometry

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