Multi-Model
What Is Multi-Model?
It refers to an approach that uses multiple models together in a single AI system.
In other words, instead of assigning everything to a single model, it combines the strengths of each model to achieve better performance or more diverse functions.
For example, it may be a model that can process not only text but also images, audio, and video together.
Why Is It Needed?
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When one model is not enough
- Example: When both images and text must be handled
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Use of specialized models
- Uses a large general-purpose model together with domain-specific models
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Performance optimization
- Heavy and slow models are used only for core reasoning, while lightweight models handle preprocessing and simple tasks
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Cost reduction
- Always using a huge model like GPT-4 is expensive, so some tasks are assigned to smaller models and only difficult parts use a larger model
Types of Multi-Model
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Different from Multi-Modal
- Multi-Model != Multi-Modal
- Multi-Modal: One model that processes multiple input forms, such as images, text, and speech
- Multi-Model: A system built by combining multiple models
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Configuration methods
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Parallel (Ensemble): Multiple models produce answers at the same time, and the results are combined to make the final decision
- Examples: Voting, blending, weighted sum
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Serial (Pipeline): The output of one model is passed as the input to another model
- Example: Image captioning model -> text summarization model -> question answering model
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Hybrid: Selects models depending on the situation, such as with a router model
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Examples
- Retrieval + generation (RAG)
- Retrieval model (vector search) + generative model (LLM)
- Copilot-style tools
- Code assistance: a small model for fast code completion, GPT-4 for sophisticated bug fixes
- Autonomous driving
- Video recognition CNN + behavior planning RL model
- Healthcare
- Medical knowledge model + general LLM combination
Multi-Model vs Single Model
| Category | Single Model | Multi-Model |
|---|---|---|
| Structure | One model performs everything | Multiple models divide roles |
| Advantages | Simple and easy to manage | Higher accuracy, more flexibility, and ability to use the latest technologies |
| Disadvantages | General-purpose models have performance limits | System is complex and requires coordination |
Summary
Multi-Model is a system design approach that combines multiple models and uses each model’s strengths to produce better results.
Examples include combining a “retrieval model + generative model,” “small model + large model,” or “specialized model + general-purpose model.”