Addressing the challenge of automated geometry math problem-solving in
artificial intelligence (AI) involves understanding multi-modal information and
mathematics. Current methods struggle with accurately interpreting geometry
diagrams, which hinders effective problem-solving. To tackle this issue, we
present the Geometry problem sOlver with natural Language Description (GOLD)
model. GOLD enhances the extraction of geometric relations by separately
processing symbols and geometric primitives within the diagram. Subsequently,
it converts the extracted relations into natural language descriptions,
efficiently utilizing large language models to solve geometry math problems.
Experiments show that the GOLD model outperforms the Geoformer model, the
previous best method on the UniGeo dataset, by achieving accuracy improvements
of 12.7% and 42.1% in calculation and proving subsets. Additionally, it
surpasses the former best model on the PGPS9K and Geometry3K datasets, PGPSNet,
by obtaining accuracy enhancements of 1.8% and 3.2%, respectively.

通过分别处理图形符号和几何基元来增强几何关系的提取，并将提取的关系转化为自然语言描述，从而高效利用大型语言模型解决几何数学问题。实验证明，GOLD 模型在 UniGeo 数据集上表现优于 Geoformer，并分别在计算和证明子集中提高了 12.7% 和 42.1% 的准确率。此外，在 PGPS9K 和 Geometry3K 数据集上也优于 PGPSNet，分别获得了 1.8% 和 3.2% 的准确率提升。