Multimodal Question

Multimodal question answering (MQA) focuses on developing AI systems that can accurately answer questions requiring the integration of information from multiple modalities, such as text, images, audio, and video. Current research emphasizes the use of large multimodal language models (MLLMs) and techniques like chain-of-thought prompting and reinforcement learning from human feedback to improve accuracy and reasoning capabilities, particularly in challenging domains like STEM education and medical diagnosis. The development of robust MQA systems has significant implications for various fields, including automated assessment, improved access to scientific literature, and enhanced human-computer interaction.