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Fine-tuning a model on many tasks phrased as natural-language instructions, so it follows new instructions well even on tasks it never saw in training.
Instruction tuning fine-tunes a base model on a large collection of tasks expressed as instructions. Wei et al. (2022) showed in FLAN that this sharply improves zero-shot performance on unseen tasks, and Ouyang et al. (2022) combined instruction data with human feedback to build InstructGPT — the recipe that turned raw language models into helpful assistants.
It is the step that makes a model good at doing what it is told, rather than merely continuing text.
Primary, peer-reviewed and archival sources for this definition.
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