Multimodal Large Language Model

Multimodal large language models (MLLMs) integrate multiple data modalities, such as text, images, and audio, to enhance understanding and reasoning capabilities beyond those of unimodal models. Current research emphasizes improving MLLM performance through refined architectures (e.g., incorporating visual grounding, chain-of-thought prompting), mitigating biases and hallucinations, and developing robust evaluation benchmarks that assess various aspects of multimodal understanding, including active perception and complex reasoning tasks. This work is significant because it pushes the boundaries of AI capabilities, leading to advancements in diverse applications like medical diagnosis, financial analysis, and robotic manipulation.