A Comparative Approach to Assessing Linguistic Creativity of Large Language Models and Humans
This work addresses the problem of evaluating linguistic creativity in AI for researchers and developers, though it is incremental as it builds on existing creativity assessment methods.
The paper introduced a linguistic creativity test for humans and LLMs, assessing word formation and metaphorical language use, and found that LLMs outperformed humans in originality, elaboration, and flexibility across most tasks.
The following paper introduces a general linguistic creativity test for humans and Large Language Models (LLMs). The test consists of various tasks aimed at assessing their ability to generate new original words and phrases based on word formation processes (derivation and compounding) and on metaphorical language use. We administered the test to 24 humans and to an equal number of LLMs, and we automatically evaluated their answers using OCSAI tool for three criteria: Originality, Elaboration, and Flexibility. The results show that LLMs not only outperformed humans in all the assessed criteria, but did better in six out of the eight test tasks. We then computed the uniqueness of the individual answers, which showed some minor differences between humans and LLMs. Finally, we performed a short manual analysis of the dataset, which revealed that humans are more inclined towards E(extending)-creativity, while LLMs favor F(ixed)-creativity.