Multimodal LLM

Multimodal Large Language Models (MLLMs) aim to integrate diverse data modalities, such as text, images, and video, into a unified framework for enhanced understanding and generation. Current research emphasizes efficient fusion of visual and textual information, often employing techniques like early fusion mechanisms and specialized adapters within transformer-based architectures, as well as exploring the use of Mixture-of-Experts (MoE) models. This field is significant due to its potential to improve various applications, including image captioning, visual question answering, and more complex tasks requiring cross-modal reasoning, while also addressing challenges like hallucinations and bias.