# Decoders Laugh as Loud as Encoders

arXiv: https://arxiv.org/abs/2509.04779 · HTML: https://mlsanity.com/paper/2509.04779/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 77 references that are in the map.
- First posted on arXiv 5 Sep 2025.

## Builds on (most cited first, 10 of 10)

- [UR-FUNNY: A Multimodal Language Dataset for Understanding Humor](https://mlsanity.com/paper/1904.06618/) (2019)
- [Let's Think Outside the Box: Exploring Leap-of-Thought in Large Language Models with Creative Humor Generation](https://mlsanity.com/paper/2312.02439/) (2023)
- [Humor Detection: A Transformer Gets the Last Laugh](https://mlsanity.com/paper/1909.00252/) (2019)
- [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)
- ["President Vows to Cut <Taxes> Hair": Dataset and Analysis of Creative Text Editing for Humorous Headlines](https://mlsanity.com/paper/1906.00274/) (2019)
- ["So You Think You're Funny?": Rating the Humour Quotient in Standup Comedy](https://mlsanity.com/paper/2110.12765/) (2021)
- [Is This a Joke? Detecting Humor in Spanish Tweets](https://mlsanity.com/paper/1703.09527/) (2017)
- [Gender Prediction in English-Hindi Code-Mixed Social Media Content : Corpus and Baseline System](https://mlsanity.com/paper/1806.05600/) (2018)
- [Deceptive Humor: A Synthetic Multilingual Benchmark Dataset for Bridging Fabricated Claims with Humorous Content](https://mlsanity.com/paper/2503.16031/) (2025)
- [PunFields at SemEval-2017 Task 7: Employing Roget's Thesaurus in Automatic Pun Recognition and Interpretation](https://mlsanity.com/paper/1707.05479/) (2017)

## Abstract (arXiv)

From the dawn of the computer, Allen Turing dreamed of a robot that could communicate using language as a human being. The recent advances in the field of Large Language Models (LLMs) shocked the scientific community when a single model can apply for various natural language processing (NLP) tasks, while the output results are sometimes even better than most human communication skills. Models such as GPT, Claude, Grok, etc. have left their mark on the scientific community. However, it is unclear how much these models understand what they produce, especially in a nuanced theme such as humor. The question of whether computers understand humor is still open (among the decoders, the latest to be checked was GPT-2). We addressed this issue in this paper; we have showed that a fine-tuned decoder (GPT-4o) performed (Mean F1-macro score of 0.85) as well as the best fine-tuned encoder (RoBERTa with a Mean of F1-score 0.86)