March 11, 2025
Multimodal Large Language Models
Multimodal Large Language Models (MLLMs) process data from different modalities like text, audio, image, and video. Compared to text-only models, MLLMs achieve richer contextual understanding and can integrate information across modalities, unlocking new areas of application. Prime use cases of MLLMs include content creation, personalized recommendations, and human-machine interaction. Examples of MLLMs that process image and text data include Microsoft’s Kosmos-1, DeepMind’s Flamingo, and the open-source LLaVA. Google’s PaLM-E additionally handles information about a robot’s state and surroundings. Combining different modalities and dealing with different types of data comes with some challenges and limitations, such as alignment of heterogeneous data,