# A Comparative Approach to Assessing Linguistic Creativity of Large Language Models and Humans

arXiv: https://arxiv.org/abs/2507.12039 · HTML: https://mlsanity.com/paper/2507.12039/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 16 Jul 2025.

## Builds on (most cited first, 6 of 6)

- [Art or Artifice? Large Language Models and the False Promise of Creativity](https://mlsanity.com/paper/2309.14556/) (2023)
- [FLUTE: Figurative Language Understanding through Textual Explanations](https://mlsanity.com/paper/2205.12404/) (2022)
- [On the Creativity of Large Language Models](https://mlsanity.com/paper/2304.00008/) (2023)
- [I Spy a Metaphor: Large Language Models and Diffusion Models Co-Create Visual Metaphors](https://mlsanity.com/paper/2305.14724/) (2023)
- [Evaluating Morphological Compositional Generalization in Large Language Models](https://mlsanity.com/paper/2410.12656/) (2024)
- [From chocolate bunny to chocolate crocodile: Do Language Models Understand Noun Compounds?](https://mlsanity.com/paper/2305.10568/) (2023)

## Built on by (most cited first, 1 of 1)

- [Where Models Converge and Humans Diverge: A Coverage Framework for Distributional Pluralism in Open-Ended Generation](https://mlsanity.com/paper/2608.05576/) (2026)

## Abstract (arXiv)

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.