Datasets:
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---
dataset_info:
features:
- name: pun
dtype: string
- name: prefix
dtype: string
- name: definition
dtype: string
- name: answer
sequence: string
- name: phonetic
dtype: int64
- name: realistic
dtype: int64
- name: typology
sequence: string
- name: __index_level_0__
dtype: int64
splits:
- name: main
num_bytes: 49417
num_examples: 350
- name: contaminated
num_bytes: 2642
num_examples: 20
- name: few_shot
num_bytes: 1382
num_examples: 10
download_size: 37114
dataset_size: 53441
configs:
- config_name: default
data_files:
- split: main
path: data/main-*
- split: contaminated
path: data/contaminated-*
- split: few_shot
path: data/few_shot-*
license: mit
task_categories:
- question-answering
language:
- en
---
# Phunny: A Humor-Based QA Benchmark for Evaluating LLM Generalization
Welcome to **Phunny**, a humor-based question answering (QA) benchmark designed to evaluate the reasoning and generalization abilities of large language models (LLMs) through structured puns.
This repository accompanies our **ACL 2025 main track paper**:
["What do you call a dog that is incontrovertibly true? Dogma: Testing LLM Generalization through Humor"](https://aclanthology.org/2025.acl-long.1117.pdf)
To reproduce our experiments: [Code available on GitHub](https://github.com/disi-unibo-nlp/Phunny)
## Overview
**Phunny** consists of 350 novel, manually curated structured puns, created through a two-stage process: creative human design followed by automated contamination checks to ensure novelty.
All puns follow the same strcuture:
```
What do you call a X that Y? XZ
```
- **X** is a prefix (subword of XZ)
- **Y** is a natural language definition of the answer XZ
- **XZ** is the pun answer (that starts with the prefix X), meant to be humorous
For example:
> What do you call a dog that is incontrovertibly true? **Dogma**
> → “Dog” (X) + “dogma” (XZ), where “dogma” means a set of incontrovertible truths.
We define three tasks to evaluate different aspects of LLM capabilities:
- **Pun Comprehension**
Can an LLM distinguish between coherent and nonsensical puns?
- **Pun Resolution**
Can an LLM infer the correct punchline based on the question?
- **Pun Generation**
Can an LLM produce novel Phunny-style puns? We test this in two modes:
- *Free*: unconstrained generation
- *Constrained*: generation based on a provided prefix X
## Data Fields
- `pun`: the complete pun (question/answer)
- `prefix`: the subject of the question/pun
- `definition`: the meaning of the question/pun
- `answer`: the punchline
- `phonetic`: whether the punchline is phonetically correlated (starts with same pronunciation) w.r.t. the prefix
- `realistic`: whether the pun itself is real
- `typology`: whether the prefix itself is a noun, adjective, or verb
## Data Splits
This dataset has 3 splits: _Main_, _Contaminated_, and _Few-shot_.
| Dataset Split | Number of Instances | Content |
| ------------- | --------------------| ------------------------------------------------------------------------------ |
| Main | 350 | set of puns used in our experiments to evaluate LLMs |
| Contaminated | 20 | list of Phunny-like puns already present on the web (excluded from our evaluation) |
| Few-shot | 10 | puns used as in-context exemples for the Resolution and Generation tasks |
# Cite article
```
@inproceedings{cocchieri-etal-2025-call,
title = "``What do you call a dog that is incontrovertibly true? Dogma'': Testing {LLM} Generalization through Humor",
author = "Cocchieri, Alessio and
Ragazzi, Luca and
Italiani, Paolo and
Tagliavini, Giuseppe and
Moro, Gianluca",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1117/",
doi = "10.18653/v1/2025.acl-long.1117",
pages = "22922--22937",
ISBN = "979-8-89176-251-0",
abstract = "Humor, requiring creativity and contextual understanding, is a hallmark of human intelligence, showcasing adaptability across linguistic scenarios. While recent advances in large language models (LLMs) demonstrate strong reasoning on various benchmarks, it remains unclear whether they truly adapt to new tasks like humans (i.e., generalize) or merely replicate memorized content. To explore this, we introduce Phunny, a new humor-based question-answering benchmark designed to assess LLMs' reasoning through carefully crafted puns. Our dataset is manually curated to ensure novelty and minimize data contamination, providing a robust evaluation of LLMs' linguistic comprehension. Experiments on pun comprehension, resolution, and generation reveal that most LLMs struggle with generalization, even on simple tasks, consistently underperforming the human baseline. Additionally, our detailed error analysis provides valuable insights to guide future research."
}
``` |