Diagram Formalization Enhanced Multi-Modal Geometry Problem Solver
This addresses the problem of poor geometric diagram understanding in MLLMs for researchers and practitioners in AI and education, but it is incremental as it builds on existing MLLM methods with a new data and integration approach.
The paper tackles the challenge of AI models understanding geometry problems that require both linguistic and visual signals by introducing DFE-GPS, a framework that integrates visual features, geometric formal language, and natural language representations, resulting in improved performance on the formalgeo7k dataset.
Mathematical reasoning remains an ongoing challenge for AI models, especially for geometry problems that require both linguistic and visual signals. As the vision encoders of most MLLMs are trained on natural scenes, they often struggle to understand geometric diagrams, performing no better in geometry problem solving than LLMs that only process text. This limitation is amplified by the lack of effective methods for representing geometric relationships. To address these issues, we introduce the Diagram Formalization Enhanced Geometry Problem Solver (DFE-GPS), a new framework that integrates visual features, geometric formal language, and natural language representations. We propose a novel synthetic data approach and create a large-scale geometric dataset, SynthGeo228K, annotated with both formal and natural language captions, designed to enhance the vision encoder for a better understanding of geometric structures. Our framework improves MLLMs' ability to process geometric diagrams and extends their application to open-ended tasks on the formalgeo7k dataset.