Multimodal large language models (MLLMs) are deep learning algorithms that can process and analyze data across multiple content types, including text, images, audio, and video, simultaneously.

They are an extension of Large Language Models (LLMs), which are trained only using text data. MLLMs, by contrast, can handle complex prompts that combine multiple input types — for example, an image paired with a question — to perform a range of tasks.

Their deep learning architecture, often based on transformer neural networks, enables MLLMs to detect sentiment in text, recognize objects in images, and interpret audio cues. Transformers are a type of model architecture that excels at processing sequences and learning contextual relationships across data.

Multimodal LLMs surpass the limitations of unimodal models (such as earlier versions of ChatGPT), which are restricted to a single data type. Unimodal models often struggle to produce cohesive responses when inputs span different modalities, making MLLMs more relevant for enterprise use cases.

What are modalities in AI?

Modalities are different types or formats of data. Multimodal models combine these modalities and analyze this varied input to perform nuanced tasks with higher accuracy. There are four common types of modality used in MLLMs or enterprise AI applications.

  • Text: Used to train models for tasks involving written language or software code. It also supports document analysis — for example, summarizing financial reports or extracting key insights from legal documents.
  • Image: Includes photos, graphics, and diagrams. MLLMs use this modality for tasks such as object recognition, image classification, reading embedded text, or enabling visual search, particularly valuable in retail environments.
  • Audio: Involves speech or environmental sounds. It is typically used for speech recognition, identifying patterns in spoken language, and interpreting human conversation, which is helpful in areas such as call center analytics or patient interactions in healthcare.
  • Video: Combines image and audio modalities. Analysis often involves interpreting frames, audio, text (such as subtitles), and contextual cues, enabling applications like monitoring customer behavior in retail or reviewing diagnostic footage in healthcare.

How do multimodal LLMs work?

Multimodal LLMs utilize multiple data modalities to perform complex tasks and generate coherent outputs.

Pretraining and instruction fine-tuning

The multimodal LLM is trained on a diverse dataset to learn relationships across modalities and identify key features that are common across them. After pretraining, the model is often fine-tuned for specific tasks to improve performance.

Embedding and representation

The model combines embeddings — numerical values that represent semantic and structural meaning — from each modality to create a representation. This representation shows the relationships and contrasts between data points and modalities. The model uses this information to provide outputs relevant to the input data context, such as an accurate visual-text representation.

Alignment and fusion

The embeddings are then aligned to ensure the model can perform tasks and fused into a unified multimodal representation that provides a comprehensive understanding. The model uses this information to ensure that responses are coherent and contextually appropriate. 

Cross-modal relationships

Diverse datasets allow the model to learn and understand the relationships between the different modalities. This enables models to generate responses or perform tasks using a combination of modalities, thereby improving workflows and efficiency. Model outputs may be more accurate as they learn to interpret contextual relevance.

Output generation

Models produce cohesive and contextually relevant responses using inference. For example, combining image and text analysis for document classification in finance or diagnostics in medical imaging. Inference allows the model to generate predictions or answers from new, unseen inputs, such as responding to specific audit queries or identifying anomalies in patient records.

Architectures and training techniques in MLLMs

Multimodal LLMs use specific architectures and training techniques to enhance their performance and efficiency.

Unified representation learning

Data across different modalities is combined into a unified representation. Data from each modality can retain its distinct characteristics. This enables the model to understand interrelationships among modalities within the unified representation. Unified representation learning enables models to generalize more effectively to new and unseen data.

Cross-modal attention mechanisms

Cross-modal attention mechanisms allow a model to use relevant context from one data modality to understand the information in another. A common example of this would be using text-based image descriptions to enable the model to accurately interpret the contents of an image.

Generative pretraining

Pretraining a model with multimodal data enables the model to learn a diverse range of scenarios and gain a broad understanding. This allows the model to be fine-tuned for specific tasks, such as image labeling for medical or retail purposes. Using generative pretraining enhances model versatility.

Data processing pipelines

Efficient data processing optimizes the model training stage. It ensures faster deployment of models, especially when training costs and time are key considerations. As datasets are diverse, this means the processing pipeline must be able to handle loading, preprocessing, and batching the data types. Processing multimodal data requires substantial computational resources to manage the load effectively.

Multimodal LLM use cases

Multimodal LLMs are suitable for a variety of enterprise applications. There is increasing demand for multimodal AI, with the market currently valued at USD 1.6 billion with a projected compound annual growth rate of 32.7% by 2034.

Clinical decision support and documentation automation

MLLMs are able to analyze medical imagery, such as X-rays and MRIs, alongside clinical notes to provide accurate diagnoses and treatment plans. They are also used to provide summaries of documents and function as interactive AI assistants for medical professionals, improving productivity and efficiency.

Multimodal customer search and personalization

E-commerce organizations can use MLLMs to help customers search for products using text, image, or voice commands. They are also useful for catalog management tasks such as tagging similar products. This enhances the customer experience by streamlining the online shopping process. MLLMs can analyze product images and behavioral data to provide personalized recommendations for purchases. They also support virtual try-on experiences by interpreting body measurements to simulate outfit appearance and generate style-based suggestions.

Document processing and fraud detection

Multimodal LLMs are effective for document scanning, parsing, and analysis. They interpret mixed content formats — including text, tables, and images — in documents such as receipts, financial statements, or regulatory filings. This enables the generation of comprehensive analyses. MLLMs can also support fraud detection by combining image and text signals — for example, flagging mismatches between scanned IDs and stored metadata.

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