Testing Language Creativity of Large Language Models and Humans

AbstractSince the advent of Large Language Models (LLMs), the interest and need for a better understanding of artificial creativity has increased.This paper aims to design and administer an integrated language creativity test, including multiple tasks and criteria, targeting both LLMs and humans, for a direct comparison. Language creativity refers to how one uses natural language in novel and unusual ways, by bending lexico-grammatical and semantic norms by using literary devices or by creating new words. The results show a slightly better performance of LLMs compared to humans. We analyzed the responses dataset with computational methods like sentiment analysis, clusterization, and binary classification, for a more in-depth understanding. Also, we manually inspected a part of the answers, which revealed that the LLMs mastered figurative speech, while humans responded more pragmatically.