AutoGeo: Automating Geometric Image Dataset Creation for Enhanced Geometry Understanding
Zihan Huang, Tao Wu, Wang Lin, Shengyu Zhang, Jingyuan Chen, Fei Wu

TL;DR
AutoGeo introduces an automated method to generate a large, diverse geometric image dataset, significantly improving AI models' ability to understand and reason about geometry in educational and research contexts.
Contribution
The paper presents AutoGeo, a novel automated approach for creating extensive geometric datasets, filling a critical gap and enhancing multimodal language models' geometric reasoning capabilities.
Findings
AutoGeo-100k contains 100,000 high-quality geometric image-text pairs.
Fine-tuning models on AutoGeo-100k improves geometric reasoning accuracy.
Enhanced performance in geometric captioning and mathematical reasoning tasks.
Abstract
With the rapid advancement of large language models, there has been a growing interest in their capabilities in mathematical reasoning. However, existing research has primarily focused on text-based algebra problems, neglecting the study of geometry due to the lack of high-quality geometric datasets. To address this gap, this paper introduces AutoGeo, a novel approach for automatically generating mathematical geometric images to fulfill the demand for large-scale and diverse geometric datasets. AutoGeo facilitates the creation of AutoGeo-100k, an extensive repository comprising 100k high-quality geometry image-text pairs. By leveraging precisely defined geometric clauses, AutoGeo-100k contains a wide variety of geometric shapes, including lines, polygons, circles, and complex spatial relationships, etc. Furthermore, this paper demonstrates the efficacy of AutoGeo-100k in enhancing the…
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Taxonomy
TopicsImage Processing and 3D Reconstruction · Geological Modeling and Analysis · Advanced Numerical Analysis Techniques
