  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.
