Multimodal Large Language Model MLLM
In the dynamic field of artificial intelligence, the emergence of multimodal large language models (MLLMs) is revolutionizing the way people interact with technology. These cutting-edge models go beyond traditional text-based interfaces and herald a new era of AI that understands and generates content in a variety of formats, including text, images, audio, and video.
Multimodal large language models are designed to process and generate multiple modalities, including text, images, and sometimes audio and video.These models are trained on large datasets containing both text and image data, allowing them to learn relationships between different modalities. Large multimodal models can be used in a variety of ways, including image captioning, visual question answering, and content recommendation systems that use text and image data to provide personalized recommendations.
Multimodal large language models combine the power of natural language processing (NLP) with other modalities such as images, audio, or video. The architecture and functionality of multimodal LLMs may vary, but they generally follow similar patterns. While large language models only take text input and produce text output, they do not directly process or generate other media modalities (such as images or videos).
The multimodal large language model includes one or more of the following methods:
- Input and output have different modalities (e.g. text to image, image to text)
- The input is multimodal (e.g. a system that can process both text and images)
- The output is multimodal (e.g. a system that can generate both text and images)
A high-level overview of how multimodal large language models work:
- The encoder for each data modality produces an embedding for the data of that modality.
- A method to align embeddings of different modalities into the same multimodal embedding space.
- (Generative models only) Language models used to generate text responses. Since the input can contain both text and visuals, new techniques need to be developed that allow the language model to condition its response not only based on the text but also the visuals.
The Importance of Large Multimodal Language Models
Multimodal language models are important because they are able to process and generate multiple types of media, such as text and images, and in some cases audio and video.
Unlike large language models that process only textual input and output, multimodal models such as GPT-4 have a remarkable ability to understand and generate content across a variety of modalities. This advance extends its usefulness to tasks involving language and vision, such as captioning images and answering questions about visual content.
In addition, multimodal models provide enhanced manipulability through customizable system messages, giving developers and users fine-grained control over the style and responses of the AI. This versatility and control makes multimodal models a key tool for creating personalized recommendations, enhancing creative content generation, and facilitating more nuanced interactions between humans and AI.