Multimodal Language Model

Definition

A multimodal language model is an extension of LLMs that can process and generate across text and other modalities such as images, audio, or video.

Purpose

The purpose is to create AI systems capable of richer understanding and interaction, beyond pure text. These models are useful for virtual assistants, accessibility tools, and robotics.

Importance

  • Supports integration of visual and auditory context in responses.
  • Powers new applications like visual question answering.
  • Computationally expensive and complex to train.
  • Shares risks of hallucination and bias from LLMs.

How It Works

  1. Collect large multimodal datasets (text + images/audio).
  2. Train with transformers adapted for multiple modalities.
  3. Align embeddings across modalities for interoperability.
  4. Fine-tune on specific multimodal tasks.
  5. Deploy for real-world multimodal interaction.

Examples (Real World)

  • GPT-4 with vision (OpenAI): processes text and images.
  • Flamingo (DeepMind): few-shot learning for multimodal tasks.
  • Google Gemini: integrates multiple modalities for reasoning.

References / Further Reading

  • Alayrac et al. “Flamingo: A Visual Language Model.” DeepMind.
  • OpenAI GPT-4 Technical Report.
  • Stanford CRFM Report on Foundation Models.