Multimodal Chain of Thoughts (COT) prompting

What is Multimodal CoT Prompting?

Multimodal CoT prompting is an advanced AI technique that combines Chain-of-Thought (CoT) prompting with multimodal inputs.
This enables AI systems to reason across multiple types of data—such as text, images, audio, and video—to achieve deeper understanding and generate more accurate results.

How Does Multimodal CoT Prompting Work?

The process generally involves the following steps:

  1. Data Collection & Processing

    • Gather information from various modalities (text, image, audio, video).

  2. Feature Extraction with Specialized Models

    • Text: BERT

    • Images: ResNet

    • Audio: wav2Vec

    Each modality is converted into high-dimensional embeddings.

  3. Fusion of Modalities

    • The embeddings are combined into a unified representation.

    • Techniques such as attention mechanisms or concatenation are applied.

  4. Chain-of-Thought Reasoning

    • The AI performs step-by-step reasoning on the integrated data.

    • Intermediate reasoning steps guide the process toward the solution.

  5. Final Output Generation

    • The reasoning chain expands into the final result, which is more context-aware and multimodally informed.