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As creative writing tasks do not have singular correct answers, large language models (LLMs) trained to perform these tasks should be able to generate diverse valid outputs.
Creative abilities in the arts
Joy P Guilford · 1957
Earlier work this paper cites.
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Linda Flower and John R. Hayes · 1981
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