  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
