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    <abstract>Addressing the challenge of automated geometry math problem-solving in artificial intelligence (AI) involves understanding multi-modal information and mathematics. blackCurrent 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.</abstract>
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%0 Conference Proceedings
%T GOLD: Geometry Problem Solver with Natural Language Description
%A Zhang, Jiaxin
%A Moshfeghi, Yashar
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F zhang-moshfeghi-2024-gold
%X Addressing the challenge of automated geometry math problem-solving in artificial intelligence (AI) involves understanding multi-modal information and mathematics. blackCurrent 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.
%U https://aclanthology.org/2024.findings-naacl.19
%P 263-278

Markdown (Informal)

[GOLD: Geometry Problem Solver with Natural Language Description](https://aclanthology.org/2024.findings-naacl.19) (Zhang & Moshfeghi, Findings 2024)

• GOLD: Geometry Problem Solver with Natural Language Description (Zhang & Moshfeghi, Findings 2024)

ACL

• Jiaxin Zhang and Yashar Moshfeghi. 2024. GOLD: Geometry Problem Solver with Natural Language Description. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 263–278, Mexico City, Mexico. Association for Computational Linguistics.