A Confederacy of Models: a Comprehensive Evaluation of LLMs on Creative Writing

Carlos Gómez-Rodríguez, Paul Williams


Abstract
We evaluate a range of recent LLMs on English creative writing, a challenging and complex task that requires imagination, coherence, and style. We use a difficult, open-ended scenario chosen to avoid training data reuse: an epic narration of a single combat between Ignatius J. Reilly, the protagonist of the Pulitzer Prize-winning novel A Confederacy of Dunces (1980), and a pterodactyl, a prehistoric flying reptile. We ask several LLMs and humans to write such a story and conduct a human evalution involving various criteria such as fluency, coherence, originality, humor, and style. Our results show that some state-of-the-art commercial LLMs match or slightly outperform our writers in most dimensions; whereas open-source LLMs lag behind. Humans retain an edge in creativity, while humor shows a binary divide between LLMs that can handle it comparably to humans and those that fail at it. We discuss the implications and limitations of our study and suggest directions for future research.
Anthology ID:
2023.findings-emnlp.966
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
14504–14528
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.966
DOI:
10.18653/v1/2023.findings-emnlp.966
Bibkey:
Cite (ACL):
Carlos Gómez-Rodríguez and Paul Williams. 2023. A Confederacy of Models: a Comprehensive Evaluation of LLMs on Creative Writing. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 14504–14528, Singapore. Association for Computational Linguistics.
Cite (Informal):
A Confederacy of Models: a Comprehensive Evaluation of LLMs on Creative Writing (Gómez-Rodríguez & Williams, Findings 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.findings-emnlp.966.pdf