  Recent advancements in large language models (LLMs) and multi-modal models
(MMs) have demonstrated their remarkable capabilities in problem-solving. Yet,
their proficiency in tackling geometry math problems, which necessitates an
integrated understanding of both textual and visual information, has not been
thoroughly evaluated. To address this gap, we introduce the GeoEval benchmark,
a comprehensive collection that includes a main subset of 2,000 problems, a 750
problems subset focusing on backward reasoning, an augmented subset of 2,000
problems, and a hard subset of 300 problems. This benchmark facilitates a
deeper investigation into the performance of LLMs and MMs in solving geometry
math problems. Our evaluation of ten LLMs and MMs across these varied subsets
reveals that the WizardMath model excels, achieving a 55.67\% accuracy rate on
the main subset but only a 6.00\% accuracy on the hard subset. This highlights
the critical need for testing models against datasets on which they have not
been pre-trained. Additionally, our findings indicate that GPT-series models
perform more effectively on problems they have rephrased, suggesting a
promising method for enhancing model capabilities.
