# Pun Unintended: LLMs and the Illusion of Humor Understanding

arXiv: https://arxiv.org/abs/2509.12158 · HTML: https://mlsanity.com/paper/2509.12158/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 47 references that are in the map.
- First posted on arXiv 15 Sep 2025.

## Builds on (most cited first, 20 of 20)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Towards Understanding Sycophancy in Language Models](https://mlsanity.com/paper/2310.13548/) (2023)
- [Measuring Association Between Labels and Free-Text Rationales](https://mlsanity.com/paper/2010.12762/) (2020)
- [We're Afraid Language Models Aren't Modeling Ambiguity](https://mlsanity.com/paper/2304.14399/) (2023)
- [Sycophancy in Large Language Models: Causes and Mitigations](https://mlsanity.com/paper/2411.15287/) (2024)
- [A Robot Walks into a Bar: Can Language Models Serve as Creativity Support Tools for Comedy? An Evaluation of LLMs' Humour Alignment with Comedians](https://mlsanity.com/paper/2405.20956/) (2024)
- [AmbiPun: Generating Humorous Puns with Ambiguous Context](https://mlsanity.com/paper/2205.01825/) (2022)
- [ExPUNations: Augmenting Puns with Keywords and Explanations](https://mlsanity.com/paper/2210.13513/) (2022)
- ["A good pun is its own reword": Can Large Language Models Understand Puns?](https://mlsanity.com/paper/2404.13599/) (2024)
- [ePiC: Employing Proverbs in Context as a Benchmark for Abstract Language Understanding](https://mlsanity.com/paper/2109.06838/) (2021)
- [Can Pre-trained Language Models Understand Chinese Humor?](https://mlsanity.com/paper/2407.04105/) (2024)
- [A Unified Framework for Pun Generation with Humor Principles](https://mlsanity.com/paper/2210.13055/) (2022)
- [How Ambiguous Are the Rationales for Natural Language Reasoning? A Simple Approach to Handling Rationale Uncertainty](https://mlsanity.com/paper/2402.14337/) (2024)

## Built on by (most cited first, 3 of 3)

- ["I See What You Did There": Can Large Vision-Language Models Understand Multimodal Puns?](https://mlsanity.com/paper/2604.05930/) (2026)
- [Harm or Humor: A Multimodal, Multilingual Benchmark for Overt and Covert Harmful Humor](https://mlsanity.com/paper/2603.17759/) (2026)
- [Refusal is Not Safety! Benchmarking Latent Safety Risks of LLM-Driven Content Humorization](https://mlsanity.com/paper/2607.15977/) (2026)

## Abstract (arXiv)

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.