  Large language models (LLMs) have shown remarkable proficiency in human-level
reasoning and generation capabilities, which encourages extensive research on
their application in mathematical problem solving. However, current work has
been largely focused on text-based mathematical problems, with limited
investigation in problems involving geometric information. Addressing this gap,
we aim to enable LLMs to solve geometric problems by understanding image input.
We first analyze the limitations of current Multimodal Large Language Models
(MLLMs) in this area: they struggle to accurately comprehending basic geometric
elements and their relationships. To overcome these challenges, we take
advantage of the unique characteristics of geometric problems (such as unique
geometric logical form, and geometric scalability) and the capacity of the
textual LLMs to build an enriched multimodal geometry dataset based on existing
data. The augmented dataset, Geo170K, contains more than 170K geometric
image-caption and question-answer pairs. Utilizing our constructed Geo170K
dataset, we develop G-LLaVA, which demonstrates exceptional performance in
solving geometric problems, significantly outperforming GPT-4-V on the
MathVista benchmark with only 7B parameters.
