What is Instruction Tuning?
TL;DR
A method of training models on diverse instruction-response pairs so they can properly follow human instructions.
Instruction Tuning: Definition & Explanation
Instruction Tuning is a technique that trains LLMs on diverse instruction-response pair data to help them accurately follow human instructions. While pre-trained LLMs learn statistical patterns from vast amounts of text, they struggle to precisely understand and respond to human intent without additional training. Instruction tuning enables appropriate responses to varied instructions like 'summarize this,' 'translate this,' and 'write code.' Google's FLAN-T5 and Meta's Llama 2-Chat are examples of models tuned with this approach. When combined with RLHF, it enables even more human-aligned outputs.