Multi Modal Large Language Model

Multi-modal large language models (MLLMs) integrate visual and textual information to perform complex tasks, aiming to bridge the gap between human-like understanding and machine intelligence. Current research emphasizes improving the consistency and fairness of MLLMs, exploring efficient fusion mechanisms (like early fusion and Mixture-of-Experts architectures), and developing benchmarks to evaluate their performance across diverse tasks, including medical image analysis and autonomous driving. This rapidly evolving field holds significant potential for advancing various applications, from healthcare diagnostics to robotics, by enabling more robust and reliable AI systems capable of handling real-world complexities.