@inproceedings{zangari-etal-2025-pun,
title = "Pun Unintended: {LLM}s and the Illusion of Humor Understanding",
author = "Zangari, Alessandro and
Marcuzzo, Matteo and
Albarelli, Andrea and
Pilehvar, Mohammad Taher and
Camacho-Collados, Jose",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1419/",
doi = "10.18653/v1/2025.emnlp-main.1419",
pages = "27936--27971",
ISBN = "979-8-89176-332-6",
abstract = "Puns are a form of humorous wordplay that exploits polysemy and phonetic similarity. While LLMs have shown promise in detecting puns, we show in this paper that their understanding often remains shallow, lacking the nuanced grasp typical of human interpretation. By systematically analyzing and reformulating existing pun benchmarks, we demonstrate how subtle changes in puns are sufficient to mislead LLMs. Our contributions include comprehensive and nuanced pun detection benchmarks, human evaluation across recent LLMs, and an analysis of the robustness challenges these models face in processing puns."
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%0 Conference Proceedings
%T Pun Unintended: LLMs and the Illusion of Humor Understanding
%A Zangari, Alessandro
%A Marcuzzo, Matteo
%A Albarelli, Andrea
%A Pilehvar, Mohammad Taher
%A Camacho-Collados, Jose
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F zangari-etal-2025-pun
%X Puns are a form of humorous wordplay that exploits polysemy and phonetic similarity. While LLMs have shown promise in detecting puns, we show in this paper that their understanding often remains shallow, lacking the nuanced grasp typical of human interpretation. By systematically analyzing and reformulating existing pun benchmarks, we demonstrate how subtle changes in puns are sufficient to mislead LLMs. Our contributions include comprehensive and nuanced pun detection benchmarks, human evaluation across recent LLMs, and an analysis of the robustness challenges these models face in processing puns.
%R 10.18653/v1/2025.emnlp-main.1419
%U https://aclanthology.org/2025.emnlp-main.1419/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1419
%P 27936-27971
Markdown (Informal)
[Pun Unintended: LLMs and the Illusion of Humor Understanding](https://aclanthology.org/2025.emnlp-main.1419/) (Zangari et al., EMNLP 2025)
ACL
- Alessandro Zangari, Matteo Marcuzzo, Andrea Albarelli, Mohammad Taher Pilehvar, and Jose Camacho-Collados. 2025. Pun Unintended: LLMs and the Illusion of Humor Understanding. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 27936–27971, Suzhou, China. Association for Computational Linguistics.