What is a multimodal LLM (MLLM)?

In recent years, AI models like GPT and Gemini have transformed how we interact with artificial intelligence through natural language. But human communication isn’t limited to just words. We understand the world through images, sounds, gestures and more. This space is where multimodal AI comes in. 

Each modality has its own structure and requires different ways to represent and interpret information. For example, text is a sequence of words, an image is a grid of pixels and audio is a continuous waveform or spectrogram. Combining multiple modalities in a single AI system is powerful because it mirrors how humans understand the world. We don’t look at a picture without context, we describe it with words, relate it to sounds or connect it to actions. When AI systems combine these different streams of information, they gain richer context and better reasoning skills. This capability makes it possible to describe images in natural language, answer questions about videos or follow text instructions with visual input. A multimodal approach pushes AI beyond unimodal text-only chat, helping machines see, listen and communicate more like we do.

The first step in any multimodal language model is to convert raw input data from different sources into machine-understandable features. Each type of data: text, visual data (image, videos and more), audio or sensor data has its own unique structure and requires a dedicated encoder to capture its meaning.  

Because machines operate in binary, various techniques are used to translate multimedia content into a format that computers can process and understand, as outlined below. For text, tokenization breaks sentences into smaller units that are then embedded by using pretrained models like BERT (bidirectional encoder representations from transformers) or other language understanding transformer-based encoders. This approach produces dense vector representations that capture semantic information. For image inputs, advanced architectures such as vision transformers (ViT) or convolutional neural networks (CNNs) extract visual features such as shapes, colors and spatial patterns. This method is used in BLIP-2, which combines a ViT-based image encoder with a Q-Former to link vision and language.¹ For audio, specialized encoders like wav2vec or HuBERT process raw waveforms to produce representations of speech or sound cues, as seen in models designed for audio-visual tasks like VideoCoCa. This modular approach ensures that each modality’s encoder is tailored to preserve the critical information needed for the next stages.

Once the model has encoded each type of input such as breaking down text into semantic word embeddings or analyzing an image for shapes and objects, it produces high-level features. These features are patterns or summaries that capture the key meaning or structure of the original data. The next step is to align these features by mapping them into a shared space so they can interact meaningfully across modalities.

This is done through a projection step, where the abstract features from each encoder are mapped into a shared embedding space. An embedding space is a common numerical representation where text, image or audio features are converted into vectors that the model can compare and combine meaningfully. Projection is typically done by using linear transformations, learned projection heads or small neural layers. This step reshapes each modality’s feature vector into a compatible size and format. Thus, the model ensures that features from text, images and audio can interact meaningfully when they are fused. 

For example, frozen uses a visual encoder to transform images into embeddings, which are then concatenated with text embeddings before feeding into a frozen LLM for in-context multimodal learning. This model uses the combined inputs directly, without extra training, to generate answers or predictions based on the new context. Similarly, LLaMA-Adapter uses lightweight adapter modules to project image recognition features from a frozen encoder and integrate them with the language model without retraining the whole system.² Projection ensures that features from text, images and audio can interact meaningfully when fused.

Once features from each modality are projected into a common or compatible space, the model combines them to form a unified multimodal representation. This step can be done by using simple strategies like concatenation, which stacks feature vectors side by side. More sophisticated approaches involve learned interactions between modalities, for example, through attention mechanisms. Cross-attention allows one modality (such as text) to selectively focus on relevant parts of another (such as an image), helping the model dynamically align and integrate information. In modern multimodal models, such mechanisms are central to representation learning, not just a final fusion step.

For instance, Flamingo and BLIP-2 employ cross-attention to align descriptive words with objects in an image. Some models use hierarchical fusion, merging features in stages. ALLaVA, for example, goes further with graph-based fusion, constructing explicit relation graphs to represent structured cross-modal links. This shared semantic space allows the model to reason across modalities for a comprehensive understanding.

Once fused, the combined features need to be refined and deeply processed to capture subtle cross-modal dependencies. For example, the layers in a transformer play a crucial role here, stacking self-attention and feedforward operations to model complex relationships. Self-attention helps refine context within the same modality, for instance, understanding the relationship between words in a sentence. Cross-attention goes a step further by allowing elements from one modality, such as text tokens, to directly interact with elements from another, like image regions. This mechanism enables the model to answer questions about an image, generate a caption or relate audio cues to visual scenes. LXMERT uses cross-attention for visual question answering (VQA) by aligning objects in an image with language tokens. VideoCoCa takes this a step further by connecting visual frames with spoken or written text, making it better at understanding videos.

After the fused features are processed, the model must produce an output that solves a specific task handled by an output decoder. For tasks like image captioning or video description, the decoder generates coherent text that describes visual or audio inputs. For example, MiniGPT-4 can create captions and follow instructions by turning combined features into natural, easy-to-understand text. Visual ChatGPT, built on OpenAI technology, uses an integrated prompt manager and multiple computer vision models. It can handle complex multistep tasks such as describing images, answering visual questions and generating new visuals from text instructions. For classification tasks, like emotion recognition or object detection, decoders map multimodal features to labels or decisions, ensuring the model’s understanding is delivered in a usable format.

Behind the scenes, the power of multimodal models comes from large-scale pretraining and task-specific prompting. These systems are trained on huge paired datasets such as image-text pairs like in CLIP, video transcripts or audio-text pairs. CLIP famously uses contrastive learning to align images and captions, while LLaVA and MiniGPT-4 leverage synthetic instruction-following datasets generated by using GPT-4 to expand their understanding of how language and vision relate. Pretraining with tasks like masked modeling and contrastive matching builds broad cross-modal knowledge, while prompting and fine-tuning adapt these general skills to specific applications with little extra data. This method makes MLLMs capable of impressive zero-shot and few-shot performance, like describing images they have never seen or generating visuals from text.

This full pipeline of encoding, projecting, fusing, processing, decoding and pretraining is what enables modern multimodal capabilities to understand and generate rich outputs. By bringing multiple forms of information together, MLLMs bridge the gap between how machines and humans perceive the world.

A practical use case of MLLMs in action is the CONCH model (contrastive learning from captions for histopathology) in healthcare.³ CONCH is a vision-language model trained on a large, domain-specific dataset to analyze medical slides, including special stains like immunohistochemistry. By using a ChatGPT-like interface, CONCH can match pathology images with diagnostic text prompts in a zero-shot setting, This helps pathologists retrieve relevant information for conditions such as invasive carcinoma or colitis without relying on massive general datasets.

GITMol is another example of an advanced MLLM designed to handle complex molecular data by integrating various modalities such as text descriptions, molecular images and graphs representing molecular structures.⁴ This model can perform tasks like predicting chemical reactions, recognizing compound names and providing insights into molecular properties. It combines textual and visual information and facilitates a deeper understanding of molecular interactions, assists in drug discovery and accelerates research in the chemical and biological sciences. Its ability to process multimodal data allows researchers to make more accurate predictions about molecular behavior and interactions, which is crucial in developing new therapeutics and understanding biological mechanisms.

Training multimodal LLMs involves carefully designing data collection processes, choosing suitable training strategies and ensuring data quality to develop models capable of understanding and reasoning across multiple modalities.

Gathering instruction data for multimodal learning is complex and costly due to the diverse formats and tasks involved. Three main methods help scale this efficiently:

• Data adaptation: Existing high-quality datasets, such as VQA (images with questions and answers) or image-caption corpora like COCO (common objects in context dataset), are transformed into instruction-response formats. This step is done manually or semiautomatically, for example, by using language models like GPT to expand short answers into richer instructions.
  
  
• Self-instruction: Models can generate additional instruction data themselves by prompting auxiliary models or by using chain-of-thought reasoning to create new tasks and answers. This boosts dataset size without heavy manual labeling.
  
  
• Data mixture: Instruction data is often blended with large-scale language-only conversation data. This ability enables models to build strong reasoning and dialog skills alongside multimodal understanding.

Quality matters as much as scale. Noisy or repetitive data can weaken performance. Therefore, teams emphasize prompt diversity, writing instructions in varied ways to prevent overfitting. And task coverage ensures that the model learns not just captioning but also more complex tasks like visual reasoning. Filtering, deduplication and checks for alignment between modalities all help maintain data integrity.

The full training pipeline typically has three phases:

• Pretraining:⁵ The model learns to align different modalities and acquire broad knowledge from large, paired datasets like image-caption pairs (for example, COCO, LAION-5B). Tasks can include predicting masked words or aligning images and text, by using objectives like contrastive learning.
  
  
• Instruction-tuning: The model trains on explicit instruction-response pairs. For example, an image-question pair might be rephrased as “Describe the image in detail” to teach the model to follow natural prompts. This stage helps models like LLaVA or MiniGPT-4 handle real-world queries.
  
  
• Alignment tuning: Finally, the model is aligned with human preferences by using techniques like reinforcement learning with human feedback (RLHF) or direct preference optimization (DPO). Human feedback improves model performance by refining answers for relevance, factuality and tone, and reduces hallucination.

Despite rapid progress, multimodal LLMs still face several critical challenges that limit their performance and practical deployment.

Handling long contexts: Many MLLMs struggle to process long, complex sequences that mix text, images or videos. This challenge makes tasks like understanding long videos or documents with rich visuals, where end-to-end comprehension is necessary, more difficult.

Complex instruction following: Current open models often fall short when asked to handle nuanced or multistep instructions. High-quality instruction following often still relies on proprietary systems like GPT-4V.

Cross-modal reasoning: Techniques like multimodal in-context learning (M-ICL) and chain of thought reasoning (M-CoT) are still in their early stages, leaving models with limited cross-modal reasoning abilities.

Expansion to new modalities: Future models need to handle more diverse data types, for example, combining audio, visual and physiological signals in emotion recognition or multiple medical imaging techniques for diagnosis.

High computation costs: Training large multimodal models demands massive computational resources including GPUs, distributed systems and careful scheduling, making it costly and time-consuming.

Lifelong learning: Most MLLMs still rely on static training. Building models that can learn continuously, adapt to new tasks and retain previous knowledge without forgetting, is an open challenge.

Safety and robustness: Like text-only LLMs, multimodal models can generate biased or misleading outputs if not properly safeguarded.

Multimodal LLMs are expanding what AI can understand and generate, but the next advancements won’t come from just adding more parameters. New architectures promise faster, more efficient sequence processing by breaking away from the attention-heavy bottlenecks of traditional transformers. These advancements mean more tokens, less computation and better scalability for long-context, richly multimodal tasks.

At the same time, smarter prompt engineering is reducing the need for costly fine-tuning by letting us guide models with better instructions instead of more training. And instead of endlessly scaling up model size, many researchers now focus on using larger, more diverse datasets enabling open-source and task-specific models to thrive.

With organizations like OpenAI, Microsoft and the open source community leading the charge, the future of multimodal AI is incredibly exciting. Innovations like retrieval-augmented generation (RAG), in context learning (ICL) and multimodal reasoning are setting new benchmarks for advancements in robotics, image recognition and language understanding. By effectively processing and integrating diverse multimodal inputs, these models are moving beyond unimodal systems to deliver richer, more context-aware experiences. Through advanced machine learning techniques and streamlined training processes, multimodal AI models are enabling smarter interactions, whether it's through conversational chatbots, intuitive image analysis or decision-making systems powered by autoregressive architectures. These systems are becoming more intuitive, context-aware and human-like, with the potential to make a real difference in industries ranging from healthcare to automation and beyond.