Instruction Tuning
Fine-tuning a model on examples of following instructions to improve its ability to understand and execute user requests.
In-Depth Explanation
Instruction tuning trains language models to follow natural language instructions more reliably. It's a key step that makes models useful assistants rather than just text predictors.
Instruction tuning process:
- Start with pre-trained base model
- Collect diverse instruction-following examples
- Fine-tune model on these examples
- Evaluate on held-out instructions
Instruction data types:
- Question answering
- Summarisation requests
- Task descriptions
- Format specifications
- Multi-step instructions
- Role-playing scenarios
Key instruction tuning methods:
- FLAN: Fine-tuned LAnguage Net (Google)
- InstructGPT: Instruction-following with RLHF (OpenAI)
- Alpaca/Vicuna: Open-source instruction tuning
- Self-instruct: Generate training data from model
Impact of instruction tuning:
- Better zero-shot performance
- More consistent outputs
- Better format following
- Safer, more helpful responses
Business Context
Instruction-tuned models are more useful for business applications, following directions reliably rather than just completing text.
How Clever Ops Uses This
We use instruction-tuned models for Australian business AI, and help clients fine-tune models on their specific instruction patterns when needed.
Example Use Case
"Using an instruction-tuned model that reliably follows "Summarise this email in 2 bullet points" vs a base model that might continue the email text."