In Depth
Instruction tuning fine-tunes a pre-trained language model on a dataset of instruction-response pairs, teaching it to understand and follow human instructions. Before instruction tuning, base language models can only predict the next token and tend to continue text rather than answer questions or follow commands. After instruction tuning, they become responsive assistants that can handle diverse tasks described in natural language.
The training data typically consists of thousands to millions of examples covering diverse tasks: answering questions, summarizing text, writing code, translating languages, and more. Each example pairs an instruction with the desired response. Key datasets include FLAN, Alpaca, and OpenAssistant. The quality and diversity of instruction data significantly impacts the model's ability to generalize to new, unseen instructions.
Instruction tuning was a crucial breakthrough that transformed language models from text predictors into useful tools. Models like FLAN-T5, Alpaca, and Vicuna demonstrated that relatively small amounts of instruction data could dramatically improve a base model's helpfulness. Most modern chat-style AI assistants undergo instruction tuning as a key step in their training pipeline, typically followed by preference optimization (RLHF or DPO) for further alignment.