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.

通过对大量不同类别的创造性指示进行收集，实现了使用深度学习语言模型（pretrained language models）从 inference-time 自然语言指示中执行新任务的可能性并验证了其用于数据集扩展和多样化的价值。