Multimodal Question Answering

Multimodal question answering (MQA) focuses on developing AI systems capable of understanding and responding to questions that incorporate multiple data modalities, such as text, images, and 3D scenes. Current research emphasizes creating robust benchmarks and datasets to evaluate models' abilities to handle complex reasoning across modalities, particularly in challenging domains like finance and 3D scene understanding, often employing large language models (LLMs) and graph neural networks (GNNs) within retrieval-augmented generation (RAG) frameworks. These advancements are crucial for improving AI's ability to interact with the real world and have significant implications for applications ranging from customer service chatbots to autonomous navigation systems.