Instruction Tuning
term_id: instruction_tuning
Category: training_techniques
Definition
This process bridges the gap between general pre-training and specific task performance. By exposing the model to diverse instruction-response pairs, it learns to generalize to unseen tasks without additional architectural changes. It significantly enhances the model’s ability to follow complex directions, perform zero-shot learning, and align with human preferences compared to base models.
Summary
Instruction tuning is a fine-tuning technique where a pre-trained language model is trained on a dataset of instructions and their corresponding responses to improve task-following capabilities.
Key Concepts
- Fine-tuning
- Supervised Learning
- Zero-shot Generalization
- Human Alignment
Use Cases
- Building chatbots
- Improving code generation accuracy
- Aligning models with safety guidelines