  Instruction tuning enables pretrained language models to perform new tasks
from inference-time natural language descriptions. These approaches rely on
vast amounts of human supervision in the form of crowdsourced datasets or user
interactions. In this work, we introduce Unnatural Instructions: a large
dataset of creative and diverse instructions, collected with virtually no human
labor. We collect 64,000 examples by prompting a language model with three seed
examples of instructions and eliciting a fourth. This set is then expanded by
prompting the model to rephrase each instruction, creating a total of
approximately 240,000 examples of instructions, inputs, and outputs.
Experiments show that despite containing a fair amount of noise, training on
Unnatural Instructions rivals the effectiveness of training on open-source
manually-curated datasets, surpassing the performance of models such as T0++
and Tk-Instruct across various benchmarks. These results demonstrate the
potential of model-generated data as a cost-effective alternative to
crowdsourcing for dataset expansion and diversification.
