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

翻译：大语言模型在数学问题求解领域已得到广泛应用。然而，在通常需要视觉辅助以更好理解的几何问题中，即使当前最先进的多模态模型仍面临有效利用图像信息的挑战。高质量数据对于增强多模态模型的几何能力至关重要，但现有开源数据集及相关工作要么对直接模型学习而言过于困难，要么存在文本与图像之间的不对齐问题。为克服这一难题，我们提出了一种创新流程，利用GPT-4和GPT-4V生成文本与图像对齐的相对基础几何问题，从而促进模型学习。我们构建了包含4.9K个几何问题的数据集，并将其与19K开源数据结合形成GeoGPT4V数据集。实验结果表明，GeoGPT4V数据集显著提升了各类模型在MathVista和MathVision基准测试中的几何性能。代码已发布于https://github.com/Lanyu0303/GeoGPT4V_Project