Instruction tuning is a training or fine-tuning method that teaches a model to follow natural language instructions more reliably. It typically uses datasets of prompts and desired responses so the model learns to perform tasks according to user intent and stated constraints.
A form of fine-tuning that improves a generative AI model's ability to follow instructions. Instruction tuning involves training a model on a series of instruction prompts, typically covering a wide variety of tasks. The resulting instruction-tuned model then tends to generate useful responses to zero-shot prompts across a variety of tasks. Compare and contrast with: - parameter-efficient tuning - prompt tuning