Multimodal Language Model

A Multimodal Language Model is a language model that can process and generate output using multiple modalities (such as text, image, audio, and video).

• Context:
  • It can (typically) be a Foundation Model through pre-training process.
  • It can (typically) be a Large Language Model through neural network architecture.
  • It can process Multiple Input Types through multimodal encoders.
  • It can generate Multiple Output Types through multimodal decoders.
  • It can incorporate:
    • Text Processing for natural language input and text generation.
    • Image Processing for visual content analysis and image understanding.
    • Audio Processing for speech recognition and audio analysis.
    • Video Processing for temporal content analysis and motion understanding.
  • It can range from being a Dual-Modal Model to being an Omni-Modal Model, depending on its supported modality count.
  • ...
• Example(s):
  • Vision-Language Models, such as:
    • GPT-4V for text and image processing.
    • PaLM-E for robotics vision and language.
  • Audio-Language Models, such as:
    • Whisper for speech to text conversion.
    • AudioCraft for text to audio generation.
  • Multi-Modal Models, such as:
    • GPT-4o with text, image, and audio capabilitys.
    • Claude 3 Opus with text and image understanding.
  • ...
• Counter-Example(s):
  • Text-Only Language Model, which processes only text input.
  • Vision-Only Model, which processes only image input.
  • Audio-Only Model, which processes only audio input.
• See: Multimodal Encoder, Cross-Modal Attention, Modal Fusion, Multimodal Training Data.

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