Large language models (LLMs) are highly efficient at interpreting and generating text. But they can't parse can’t parse other types of information, such as images or video. Multimodal models (also called large multimodal models or LMMs) represent the fuller next generation of AI, as they are capable of handling a variety of modalities, including text, images, audio and video. In this article, we’ll explore multimodal models and their benefits. We’ll also provide several examples of how organizations use multimodal models to encompass complex challenges beyond what LLMs can address.
What Are Multimodal Models?
Multimodal models are models that can process and generate multiple types of data, such as text, images, audio, video and Internet of Things sensor data. Their key advantage is that they can leverage complementary information in different modalities to enable a fuller understanding and representation of the data. LMMs can also produce more than one type of output, generating audio, visual and text responses to user prompts.
LLMs vs. LMMs
Multimodal models represent a significant evolution in the development of AI models. Simple unimodal models can only work with one data type. Multimodal models, on the other hand, can ingest and interpret information across multiple modalities. Complex neural networks enable these models to learn joint representations from images, audio and more, making them more capable, accurate and versatile than their text-dependent counterparts. LMMs are an integral part of generative AI applications, and multimodal models allow users to incorporate images and video into their prompts to understand the context and meaning of the data better than would be possible with a unimodal model.
Capabilities of Multimodal Models
Multimodal models provide several key advantages over traditional unimodal approaches. Here are a few of their capabilities.
Comprehensive understanding and generation
Multimodal models can interpret and generate data in different modalities to perform a task, such as generating captions from images, generating images from text descriptions, or generating video summaries from multimodal data. They can also translate between modalities, such as translating speech to text or text to sign language.
Multimodal fusion and reasoning
By combining information from different modalities and applying reasoning to the data as a whole, multimodal models can successfully perform a wider variety of task types. For example, they can use a range of data types to perform tasks like visual question-answering, multimodal sentiment analysis or multimodal dialogue systems.
Multimodal retrieval and matching
Multimodal models can retrieve relevant information from one modality based on a query in another modality, such as retrieving images based on text descriptions or vice versa. They can also match and align data across different modalities, such as aligning spoken words with lip movements in videos.
Multimodal data augmentation and generation
Synthetic multimodal data can be created by combining or converting information across modalities, which can be useful for data augmentation or domain adaptation.
Multimodal transfer learning and domain adaptation
Multimodal models make it possible to transfer knowledge learned from one modality or domain to another, leveraging the shared representations and complementary information across modalities.
Improved understanding and accuracy
By using a diverse set of prompts, LMMs can reduce errors, improve performance and generate more comprehensive insights.
Use Cases for Multimodal Models
One reason large multimodal models are so significant is that they can be applied to a wide variety of use cases across industries. Here are a few examples.
Multimodal sentiment analysis
LLMs can perform sentiment analysis to extract information about a user's sentiment and emotions from textual data, but they can’t provide context. LMMs address blind spots by combining textual data with visual or audio cues. Integrating facial expressions, tone of voice and body language with textual data can result in a deeper, more nuanced understanding of the emotional state and intent embedded within a communication.
This in turn enables more accurate sentiment classification and analysis. Customer feedback analysis is one application. Multimodal models can leverage video reviews, audio from customer service calls, and textual data from review sites or online chat transcripts to provide a more comprehensive and accurate representation of the derived insights.
Content and product recommendation
Multimodal models can help organizations improve content and product recommendation systems by taking visual data into account. Understanding what images users see, in addition to the text they read, can help companies gain valuable clues about a user’s preferences, enabling them to deliver more relevant and valuable recommendations. Multimodal models can also improve the quality of content recommendations by enriching textual data such as reviews and tags with visual data such as video clips.
Medical image analysis
Multimodal models can be used in healthcare to create a more comprehensive view of patients. Physicians can improve diagnosis and treatment planning by using multimodal models, which can analyze medical images such as X-rays and CT scans alongside information about risk factors, data transmitted from remote patient monitoring devices and other medical information.
Quality control
Multimodal models help manufacturers maintain high quality standards by enabling them to identify and correct quality control issues rapidly. These models can combine visual inspection of images or videos with temperature, acoustic and vibration data transmitted from equipment-mounted sensors. With this information, manufacturers can dynamically adjust production processes to improve product quality and customer satisfaction, reducing the number of rejected or reworked products.
Improved accessibility for individuals with disabilities
Multimodal models open up new opportunities for those with disabilities, allowing individuals to interact with their environment using their preferred modalities. For example, multimodal models can be used to help individuals with visual impairments. An LMM can rapidly analyze real-time visual inputs — such as images of a room, city sidewalk or other unfamiliar environment — and use that to provide individuals with a detailed description of nearby objects and potential hazards.
Snowflake + Reka: Bringing Multimodal LLMs to Snowflake Cortex AI
Snowflake is committed to helping our customers make better decisions, improve productivity and reach more customers using all types of data. Snowflake Cortex AI is efficient, easy to use and trusted across enterprises. The Snowflake partnership with Reka supports its suite of highly capable multimodal models in Snowflake Cortex AI. This will allow customers to gain value from more types of data, thanks to having the power of multimodal AI in the same environment where their data lives — protected by the built-in security and governance of the Snowflake AI Data Cloud.