Definition
This term refers to the broader application paradigm where models with billions of parameters are leveraged for zero-shot or few-shot learning across diverse linguistic tasks. Unlike specialized models, LLMs serve as general-purpose engines that can be prompted to perform various functions without task-specific retraining, shifting the focus from model architecture design to prompt engineering and fine-tuning strategies.
Summary
The paradigm of using scaled neural networks for broad-spectrum natural language understanding and generation tasks.
Key Concepts
- Zero-Shot Learning
- Prompt Engineering
- Generalization
- Parameter Scale
Use Cases
- Multi-turn Dialogue Systems
- Semantic Search
- Data Extraction