LearnAI

Navigation

Introduction to AI

Machine Learning

Deep Learning

NLP & LLMs

Computer Vision

Generative AI

Reinforcement Learning

AI Ethics & Governance

Tools & Frameworks

General

Multimodal AI Models

Multimodal AI models can understand and generate content across more than one modality — most commonly text and images, but increasingly audio, video, and structured data as well. Models like GPT-4o, Gemini, and Claude are multimodal, accepting mixed inputs and producing outputs that may span modalities.

What Is a Modality?

A modality is a type or format of data:

  • Text: Language, code, structured data.
  • Images: Photos, diagrams, screenshots.
  • Audio: Speech, music, environmental sounds.
  • Video: Sequences of frames with audio.
  • 3D / sensor data: Point clouds, depth maps, tabular sensor readings.

How Multimodal Models Work

Most multimodal LLMs use a modality encoder + language model design:

  1. A specialized encoder (e.g., a Vision Transformer for images, Whisper for audio) converts non-text inputs into embeddings.
  2. These embeddings are projected into the language model’s token space via a learned adapter layer.
  3. The language model processes the combined token sequence and generates a text response.

For image generation, a separate decoder (diffusion model or autoregressive image model) is often attached.

Notable Models

  • GPT-4o: Natively processes text, images, and audio in a unified model.
  • Gemini 1.5 Pro: Handles text, images, audio, video, and code in a 1M token context.
  • Claude 3.5: Text and image input, text output.
  • LLaVA: Open-source multimodal model connecting a CLIP vision encoder to Llama.
  • Whisper: OpenAI’s speech-to-text model, widely used as an audio encoder in multimodal pipelines.

Key Capabilities

  • Visual question answering: Answer questions about images.
  • Document understanding: Parse PDFs, forms, receipts, screenshots.
  • Image captioning and description.
  • Audio transcription and spoken dialogue.
  • Video understanding: Summarize or answer questions about video content.
  • Chart and diagram analysis.

Challenges

  • Alignment across modalities: Ensuring the model correctly grounds language in visual or audio content.
  • Hallucination in vision: Models can misread text in images or hallucinate visual details.
  • Training data complexity: Requires large, paired multimodal datasets.
  • Evaluation: Measuring multimodal understanding is harder than text-only benchmarks.