Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor
Or Honovich, Thomas Scialom, Omer Levy, Timo Schick · 2023
Instruction tuning enables pretrained language models to perform new tasks from inferencetime 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. Example 1Instruction: You are given a science question (easy-level) and four answer options (associated with "A", "B", "C", "D").Your task is to find the correct answer based on scientific facts, knowledge, and reasoning.Do not generate anything else apart from one of the following characters: 'A', 'B, 'C', 'D'.There is only one correct answer for each question. Input: Which part of a bicycle BEST moves in a circle? (A) Seat (B) Frame (C) Foot pedal (D) KickstandConstraints: The output should be one of the following characters: 'A', 'B, 'C', 'D'. Example 2Instruction: You are given a negative review and your task is to convert it to a positive review by one or more making minimal changes.Avoid changing the context of the review.Input: we stood there in shock, because we never expected this. Constraints: None.Example 3 Instruction: In this task, you are given two sentences taken from a conversation, and your job is to classify whether these given sentences are sequential or not.We will mark the given sentence pair as 'True' if it's sequential, otherwise 'False'.The two sentences are spoken by two different people.