AI Dictionary › AI Fundamentals
Instruction tuning is a training phase in which a language model is refined on a set of examples made of instructions and correct responses, teaching it to follow commands expressed in natural language. Unlike pre-training, which teaches the model to predict the next piece of text, this phase teaches it to behave like an assistant that carries out specific requests. It is one of the steps that turns a base model into something usable in a conversational product.
The model is exposed to many instruction-response pairs covering different tasks: answering questions, summarizing text, writing code, translating. Through this supervised training, the model generalizes the behavior of following instructions even to tasks not seen during tuning, as long as they are structurally similar to the ones observed.
It is a common step in building conversational assistants based on LLMs, often combined with later techniques such as RLHF to further refine response style and safety. It is applied both by those who build base models and by those who customize them for a specific domain.
It is associated with the shift of language models from simple text-completion systems to assistants capable of executing instructions, a key change in the recent evolution of generative models.
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