Multi Modal Language Model

Multi-modal language models (MLMs) aim to integrate information from various modalities, such as text, images, audio, and video, to improve understanding and generation capabilities beyond those of unimodal models. Current research focuses on developing efficient architectures, like hierarchical transformers and Perceiver models, and improving training strategies, including instruction tuning and knowledge distillation, to enhance performance on tasks such as visual question answering, image captioning, and speech recognition. These advancements hold significant promise for applications in diverse fields, including healthcare, robotics, and creative content generation, by enabling more sophisticated and contextually aware AI systems.