Putting GPT-3’s Creativity to the (Alternative Uses) Test. (arXiv:2206.08932v1 [cs.AI])

AI large language models have (co-)produced amazing written works from
newspaper articles to novels and poetry. These works meet the standards of the
standard definition of creativity: being original and useful, and sometimes
even the additional element of surprise. But can a large language model
designed to predict the next text fragment provide creative, out-of-the-box,
responses that still solve the problem at hand? We put Open AI’s generative
natural language model, GPT-3, to the test. Can it provide creative solutions
to one of the most commonly used tests in creativity research? We assessed
GPT-3’s creativity on Guilford’s Alternative Uses Test and compared its
performance to previously collected human responses on expert ratings of
originality, usefulness and surprise of responses, flexibility of each set of
ideas as well as an automated method to measure creativity based on the
semantic distance between a response and the AUT object in question. Our
results show that — on the whole — humans currently outperform GPT-3 when it
comes to creative output. But, we believe it is only a matter of time before
GPT-3 catches up on this particular task. We discuss what this work reveals
about human and AI creativity, creativity testing and our definition of
creativity.

Source: https://arxiv.org/abs/2206.08932

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