Large Multimodal Models

What are Large Multimodal Models?

Large Multimodal Models (LMMs), also known as Multimodal Large Language Models (MLLMs), are advanced AI systems that can process and generate information across multiple data modalities, such as text, images, audio, and video. Unlike traditional AI models that are typically limited to a single type of data, LMMs can understand and synthesize information from various sources, providing a more comprehensive understanding of complex inputs.

These models are considered a significant innovation for businesses and other applications because they enable enhanced decision-making through more accurate predictions and insights derived from diverse data types. They also streamline workflows by automating complex processes and improve customer experiences by providing personalized interactions.

LMMs are a step towards artificial general intelligence, as they exhibit emergent capabilities like writing stories based on images or performing OCR-free math reasoning. They use techniques such as Multimodal Instruction Tuning (M-IT), Multimodal In-Context Learning (M-ICL), Multimodal Chain of Thought (M-CoT), and LLM-Aided Visual Reasoning (LAVR) to achieve their functionality.

The development of LMMs is driven by the integration of additional modalities into Large Language Models (LLMs), which allows them to understand the world through multiple senses, leading to more sophisticated reasoning abilities and continuous learning capabilities.

Researchers and companies are actively exploring LMMs, with models like OpenAI's GPT-4 and DALL-E 3 being notable examples. These models are pushing the boundaries of AI by not only processing multimodal inputs but also generating multimodal outputs, which could include text, images, and potentially even animations or audio.

The field of LMMs is rapidly evolving, with new ideas and techniques being developed to improve their performance and capabilities. As these models become more advanced, they are expected to play a central role in the next generation of AI applications, offering more natural and intuitive ways for humans to interact with technology.

Examples of Large Language Models

Large Multimodal Models (LMMs) are a recent development in AI that combine different types of data, such as text and images, to generate more comprehensive outputs. Here are examples of three such models:

1. GPT-4-V — This model, developed by OpenAI, allows users to upload an image as an input and ask a question about the image, a task known as visual question answering (VQA). It can analyze image inputs provided by the user and generate a response. However, it has been noted that GPT-4-V can sometimes make mistakes in its responses and may miss mathematical symbols.

2. Google Gemini — Gemini is a large language model developed by Google that works across various Google products, including search and ads. It uses a new code-generating system called AlphaCode 2 and is designed to be more efficient, being both faster and cheaper to run than previous models. Gemini comes in different versions, including Gemini Nano for running natively and offline on Android, Gemini Pro for powering Google AI services, and Gemini Ultra, the most powerful version.

3. LLaVA — LLaVA (Large Language-and-Vision Assistant) is an end-to-end trained large multimodal model that connects a vision encoder and a large language model for general-purpose applications. It is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. LLaVA achieves 85.1% relative score compared with GPT-4, indicating the effectiveness of the proposed self-instruct method in multimodal settings.

These models represent the cutting edge of multimodal AI, each with their unique strengths and applications. They are pushing the boundaries of how we use AI by integrating different types of data to generate more comprehensive and useful outputs.

What are the differences between GPT-4V and Gemini?

GPT-4-V and Gemini are both large multimodal models, but they have distinct characteristics and capabilities.

These differences highlight the unique strengths of each model and their suitability for different tasks and applications.