training-free
term_id: training_free
Category: training_techniques
Definition
Training-free approaches refer to techniques that modify model behavior or output without updating the underlying weights via backpropagation. These methods often leverage prompt engineering, feature manipulation, or external knowledge retrieval to improve performance on specific tasks. They are valuable for reducing computational costs and avoiding catastrophic forgetting, allowing rapid adaptation to new domains using pre-trained models directly.
Summary
Methods that adapt or enhance models without performing gradient-based parameter updates.
Key Concepts
- Prompt engineering
- No gradient descent
- Parameter-efficient
- Zero-cost adaptation
Use Cases
- Few-shot learning via prompting
- Domain adaptation without fine-tuning
- Rapid prototyping of LLM applications