Math-LLaVA: A LLaVA-1.5-based AI Model Fine-Tuned with MathV360K Dataset
Why do MLLMs struggle with visual mathematical problems?
MLLMs struggle with visual mathematical problems due to limitations in visual comprehension and inadequate training on integrating visual and textual data. Existing datasets often contain insufficient visual information, and current evaluation benchmarks may not effectively assess models' true visual understanding. This highlights the need for improved datasets and methodologies for developing MLLMs with stronger multimodal reasoning capabilities.
What tasks can MLLMs perform with enhanced accuracy?
MLLMs can perform tasks such as visual question answering, text-to-image generation, and image-to-text generation with enhanced accuracy. They can also solve complex mathematical problems involving visual content and classify fine-grained categories in images.
How do MLLMs integrate visual and textual data?
MLLMs integrate visual and textual data by leveraging advanced deep learning architectures that allow them to analyze images and text simultaneously. They extract relevant features from both modalities and synthesize them into a cohesive understanding, enabling them to perform tasks such as generating captions for images and answering questions about visual content5.