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 introduceUnnatural 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.111We make our data publicly available:https://github.com/orhonovich/unnatural-instructions