How AI Learns to See and Read Together with Multimodal LLMs?

2025-11-17

Discover how multimodal LLMs combine vision and language to automate document processing, enhance reasoning, and transform enterprise workflows with AI.

1. What Are Multimodal LLMs?

Traditional Large Language Models (LLMs) like Llama or Mistral process only one type of data: text. They excel at reasoning, summarizing, and generating language—but they cannot directly interpret images, videos, or audio.

Multimodal LLMs, however, integrate multiple data types (modalities) such as text, images, audio, or video.
In this article, we focus on the most transformative combination: text + images.

A multimodal model can both see (pixels) and read (text within the image). This enables advanced capabilities such as document understanding, visual question answering, and medical image interpretation.

Figure 1: You can send an image and talk about it [1]
Figure 1: You can send an image and talk about it [1]

2. Real-World Use Cases

Multimodal models are appearing in every domain that blends language with visual information. Examples include:

Image Captioning

Given a picture, the model produces a natural-language description.

Visual Question Answering (VQA)

Ask: “What brand is the car?” or “How many people are here?”
The model visually analyzes the image and answers.

Chart, Diagram, and Table Parsing

Extract numbers from charts, identify anomalies, or convert tables to Markdown or LaTeX.

Ask questions that mix text and visuals, such as:
“Does the chart in this slide support the argument below?”

This is a step toward genuine multimodal cognition.

3. How Do Multimodal LLMs Work?

Under the hood, these systems merge computer vision and language modeling in unified transformer architectures.

Figure 2: Two Methods of Multimodal Language Models [2]
Figure 2: Two Methods of Multimodal Language Models [2]

There are two dominant patterns.

Method A: Unified Embedding + Decoder Architecture

Both the text and image are encoded into a single sequence of tokens processed by an LLM decoder (e.g., GPT-style, Llama, Gemma).

Step 1: Image Encoding

A Vision Transformer (ViT) or CLIP encoder splits the image into patches, generating embedding vectors.

Figure 3: A classic Vision Transformer (ViT) setup [3]
Figure 3: A classic Vision Transformer (ViT) setup [3]

Step 2: Linear Projection

Maps image embeddings to the same dimensionality as text embeddings.

Figure 4: Projecting 256-d image tokens into 768-d text embedding space [2]

Step 3: Concatenation + Decoding

Visual tokens and text tokens are merged and fed together into the LLM.

Figure 5: Image and text tokenization side-by-side with a projector [2]

This is used by LLaVA, OpenFlamingo, and Fuyu (the latter learns its own patch embeddings).

Method B: Cross-Modality Attention Architecture

Instead of concatenation, the text model attends to image embeddings through cross-attention layers.

This resembles the original encoder–decoder interaction from Attention Is All You Need.

Figure 6: Cross-attention in the original transformer [4]
Figure 7: Regular self-attention [2]
Figure 8: Cross-attention with two different inputs x1 (text) and x2 (image) [2]

In the original Transformer, x1 is the decoder input and x2 comes from the encoder.
In multimodal models, x2 comes from an image encoder.

Advantages of Method B

Efficiency: Image embeddings appear only where required.
Modularity: Text-only performance remains strong.
Flexibility: Easy fine-tuning on domain-specific visual data (charts, documents, photos).

This architecture powers Gemini 1.5 Pro, GPT-4V, and NVLM (NVIDIA).

4. Training Multimodal LLMs

Training unfolds in two stages.

Phase 1: Pretraining

Start with pretrained LLM + pretrained image encoder (e.g., CLIP).
Freeze everything except a small projector.
Align image and text embeddings into a shared representation space.

Phase 2: Instruction Fine-Tuning

Unfreeze selective LLM layers.
Train on multimodal instruction datasets (VQA, captioning, OCR reasoning, etc.).
Teach the model to follow prompts involving images.

Many leading systems apply RLHF or contrastive alignment to reduce hallucinations and ensure factual accuracy.

Figure 9: Alignment and hallucination reduction in Gemini [5]

Why Multimodality Matters for Enterprises

Most enterprise workflows rely on visual documents:

invoices
receipts
contracts
dashboards
diagrams
medical scans

Previously, these required split pipelines: OCR + LLM.

Now, multimodal LLMs unify the entire process—end-to-end.

Benefits

Higher automation (no heuristic OCR rules)
Contextual reasoning across layout, text, and visuals
Lower latency via unified models
Natural UX: “Upload your document and ask what it means.”

5. MDP AI Expense Portal

A real-world example: our MDP AI Expense Portal, a production-ready multimodal document-understanding system.

How It Works

Employees upload receipt or invoice images.
Multimodal AI extracts vendor, date, VAT, totals, and line items—even from low-quality or handwritten images.
Structured data fills expense forms automatically.
Managers review and approve via an integrated dashboard.

This system blends Vision Transformers, OCR fine-tuning, and instruction-aligned LLM reasoning to convert messy financial documents into high-quality structured records.

Results

Major reduction in manual data entry
Significantly lower error rates compared to traditional OCR
Faster and more transparent expense approval cycles

A practical, production example of multimodal LLMs transforming enterprise operations.

6. Looking Ahead

Multimodal LLM research is accelerating. Models like Gemini 2.5 demonstrate more efficient, data-driven, instruction-tuned multimodality.

As models integrate vision, text, audio, and even real-time interaction, we move closer to generalist AI systems.

For enterprises: smarter automation
For developers: unified multimodal APIs
For users: AI that understands both what we say and what we show

If you want to see how multimodal AI can transform your workflows today, our MDP AI Expense Portal is the perfect real-world example.

Let’s explore what multimodal AI can do for your business:
https://mdpgroup.com/en/contact-us/

References

[1] https://arxiv.org/pdf/2302.14045
[2] https://sebastianraschka.com/blog/2024/understanding-multimodal-llms.html
[3] https://arxiv.org/abs/2010.11929
[4] https://arxiv.org/abs/1706.03762
[5] https://x.com/zswitten/status/1948483504481964382