Instruction Tuning: Teaching Models to Follow Orders
Instruction tuning teaches a language model to generalize by finetuning it on a massive collection of tasks described in plain English. This transforms a raw pretrained model, which just predicts the next word, into one that can follow commands on unseen tasks without any examples (zero-shot). The footgun is mistaking this for simple finetuning on one task; its power comes from the sheer diversity of instructional tasks used during training.
Instruction tuning transforms a language model from a simple text completer into a general-purpose instruction-follower. It works by finetuning a pretrained model on a vast and diverse collection of tasks, each framed as a natural language command. This is how base models become helpful assistants, learning the *concept* of following instructions to dramatically improve zero-shot performance on new tasks. The original paper showed their instruction-tuned model, FLAN, surpassed the larger GPT-3 on many benchmarks. The key isn't just finetuning, but the scale and diversity of the instruction dataset; using only a few task types won't create a generally capable model.
Read the original → arXiv
- #llm
- #finetuning
- #zero-shot learning
- #flan
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