Understanding Multimodal LLMs: The Next Evolution of AI

Author(s): Abinaya Subramaniam

Originally published on Towards AI.

Discover how multimodal LLMs are transforming AI by combining text, images, audio, and video into a single reasoning system. Learn how they work, real-world applications, challenges, and why they’re the next evolution beyond text-only language models.

Artificial Intelligence is moving fast. Just a few years ago, most AI systems were narrow in scope. They could either understand text, or process images, or analyze audio, but rarely more than one at a time. Then came Large Language Models (LLMs) like GPT-3 and GPT-4, which transformed how machines read, write, and reason with natural language.

But here’s the catch. These models were still unimodal. They lived in the world of words.

Humans, on the other hand, don’t rely on one sense alone. We read and listen, we watch and speak, and we combine information from all around us to make sense of reality. The natural next step for AI was clear. Building systems that can understand and generate across multiple modalities.

This is the promise of Multimodal LLMs, models that not only read text but can also interpret images, listen to audio, analyze video, and even reason across them together.

What Exactly Are Multimodal LLMs?

A multimodal large language model is designed to process and generate information across different types of input and output.

For example, you could upload a photo of a handwritten math problem and ask the model to solve it. Or you might provide a chart alongside a written question, and the model can respond by combining visual analysis with textual reasoning. Some multimodal models can even generate new images or audio in response to natural language instructions.

In short, while traditional LLMs gave AI the ability to read and write, multimodal LLMs give AI the ability to see, hear, and explain. They bring machines closer to how humans perceive and interact with the world.

Why Do We Need Multimodal AI?

Imagine trying to follow a movie by only reading its script, or watching a football match with no commentary, just raw images. You’d miss a huge part of the meaning. That’s how unimodal AI operates, it can only handle one piece of the puzzle.

Real-world understanding is inherently multimodal. Doctors look at scans while reading patient histories. Teachers explain diagrams while answering verbal questions. Designers sketch visuals while writing notes. To be truly useful in these contexts, AI must combine different forms of information.

Some of the most impactful applications include:

• Education, where AI tutors can explain a diagram or help solve problems that involve text and images.
• Healthcare, where models can read X-rays or MRI scans alongside patient reports.
• Accessibility, where visually impaired users can upload a picture and receive a descriptive explanation in natural language.
• Creative industries, where text prompts can be turned into images, videos, or even music.

By blending modalities, multimodal LLMs move us closer to AI that doesn’t just process information, but truly understands context.

How Do Multimodal LLMs Work?

At first glance, multimodal LLMs may look like magic. Give them a picture, ask a question in text, and they reply with a coherent, contextaware answer. But under the hood, these systems rely on a careful architecture that allows them to convert different kinds of information into a shared representation that the model can reason with.

1. The Role of Encoders

Every modality, text, image, audio, video looks very different to a computer.

• Text is a sequence of words.
• Images are grids of pixels.
• Audio is a waveform that changes over time.
• Video is essentially images plus time.

To handle this, multimodal models use special encoders tailored for each modality:

• Text Encoder: Usually based on a transformer, it turns words into numerical embeddings (vectors).
• Image Encoder: Often a Vision Transformer (ViT) or a convolutional backbone that converts pixels into patch embeddings.
• Audio Encoder: Converts waveforms into spectrograms and then embeds them using transformers or CNNs.
• Video Encoder: Breaks video into frames (images) and adds temporal layers to capture motion.

These encoders serve as translators, turning raw inputs into a common machine-readable form.

2. Aligning into a Shared Embedding Space

Once encoders create embeddings, the next challenge is alignment.
The goal is to make embeddings from different modalities “speak the same language.”

For instance, the word “dog” and a photo of a dog should map to similar points in the embedding space. To achieve this, researchers use techniques like:

• Contrastive Learning: Models learn by pairing text and image that belong together (caption + picture) and pushing apart pairs that don’t. (This is how CLIP by OpenAI was trained.)
• Projection Layers: Each encoder’s outputs pass through additional layers that project them into a shared dimension, allowing comparison across modalities.

This alignment is what enables cross-modal tasks, like asking a model to find images that match a caption.

3. Fusion and Cross-Attention

Alignment gets embeddings into the same space, but reasoning requires fusion.

Fusion mechanisms bring multiple streams of information together. The most common method is cross-attention, a transformer-based mechanism where one modality can “attend” to another.

Example:

• You give an image of a cat and the question “What is the color of the animal?”
• Cross-attention lets the text tokens attend to relevant image patches (the fur of the cat), connecting the word “color” to the part of the image that contains color information.

Fusion strategies vary:

• Early Fusion: Merge embeddings right after encoding.
• Late Fusion: Process each modality separately and only combine results near the end.
• Hierarchical Fusion: Merge in layers, so information is shared gradually.

4. Reasoning with the LLM Backbone

Once the modalities are fused, the multimodal system routes everything into an LLM backbone.

This backbone (often a GPT-like transformer trained on massive text corpora) brings the reasoning ability:

• It interprets the aligned multimodal input.
• It applies world knowledge and context.
• It generates natural, human-like responses.

In some architectures, the LLM is not retrained but instead connected to pretrained encoders via adapters (e.g., BLIP-2). In others, the LLM is jointly trained with vision and audio encoders for tighter integration (e.g., Gemini).

5. Generating Outputs

The final stage depends on the task:

• Text Generation: Answering questions, writing captions, explanations.
• Image Generation: Using a diffusion model (like DALL·E or Stable Diffusion) guided by the text or multimodal input.
• Audio/Video Output: Producing speech, music, or even edited videos.

The same shared embedding space that allowed multimodal understanding now serves as the foundation for multimodal creation.

6. Example Workflow in Action

Suppose you upload a bar chart and ask, “What trend is shown in this data?”

1. The image encoder processes the chart and extracts embeddings of bars, axes, and labels.
2. The text encoder processes your question into embeddings.
3. Alignment layers bring both into the same space.
4. Cross-attention fusion links “trend” in your question to the visual elements of the chart.
5. The LLM backbone applies reasoning: it sees the upward slope and interprets it as growth over time.
6. The output generator responds: “The chart shows a steady increase in values across the years.”

From the outside, it looks like the AI “understood” the chart, but internally, it’s a carefully orchestrated dance of encoders, alignments, and transformers.

The Current Landscape of Multimodal LLMs

Several organizations are already pushing the boundaries of multimodality.

• OpenAI’s GPT-4 with Vision (GPT-4V) can analyze and interpret images alongside text queries.
• Google Gemini was built from the ground up to be multimodal, handling text, images, video, and even code.
• Anthropic’s Claude with Vision can describe and reason about images with strong attention to detail.
• Meta’s ImageBind takes it further by aligning six different modalities: text, image, audio, depth, thermal, and motion sensors.
• On the open-source side, models like BLIP-2, Flamingo, and LLaVA are giving researchers and developers a playground to explore multimodal AI.

Each of these systems has its strengths. Some are better at text-to-image generation, while others excel at visual reasoning or multimodal understanding. Together, they signal a rapid shift toward AI systems that interact with the world more broadly and deeply.

Real-World Applications

The impact of multimodal LLMs stretches across industries:

• In education, students can upload a geometry diagram and get a step-by-step explanation of the solution.
• In healthcare, radiologists can use AI as a second opinion, combining medical images with written records.
• For accessibility, someone with vision impairment could ask, “What’s in this picture?” and receive a detailed, context-aware description.
• In creative fields, artists and filmmakers can brainstorm visually with AI, turning text descriptions into image boards or animations.
• For robotics, multimodal reasoning allows machines to understand both spoken commands and visual cues from the environment.

These applications are already emerging, and as the technology matures, they will only expand.

Challenges and Ethical Concerns

Of course, the road to multimodality isn’t free of challenges.

Training data across modalities must be carefully aligned, which is no small task. Biases in text or image datasets can spill into models, reinforcing harmful stereotypes. The ability to generate realistic images and videos raises serious concerns about deepfakes and misinformation. And the sheer computational cost of training these models makes them accessible to only a few major players.

On top of that, multimodal systems are often hard to interpret. We don’t yet fully understand how the fusion of modalities works internally, which makes it difficult to guarantee fairness and safety.

Addressing these issues is critical if multimodal AI is to be trusted in sensitive fields like healthcare, law, or education.

The Road Ahead

Despite the challenges, the trajectory of multimodal AI is clear. Future models will likely incorporate even more modalities, 3D environments, touch, motion, perhaps even smell.

We may see models evolve into world models, capable of predicting and simulating how environments change over time. Smaller, domain-specific multimodal LLMs will likely emerge for industries like finance, medicine, and education, making the technology more practical and accessible.

Ultimately, multimodal LLMs bring us closer to the vision of Artificial General Intelligence (AGI), machines that can learn, reason, and interact with the world in a way that feels more human-like.

Conclusion

Multimodal LLMs represent a powerful shift in artificial intelligence. They extend the capabilities of text-only language models into new dimensions, enabling AI not just to read the world, but also to see, hear, and experience it.

If language-only models were like teaching AI to read and write, multimodal LLMs are like teaching AI to perceive reality itself.

The journey is still unfolding, but one thing is clear. Multimodal AI isn’t just an upgrade. It’s a leap forward in how machines and humans will connect, collaborate, and create in the years to come.

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Published via Towards AI