# Stimulating Creativity with FunLines: A Case Study of Humor Generation in Headlines

arXiv: https://arxiv.org/abs/2002.02031 · HTML: https://mlsanity.com/paper/2002.02031/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2020.

## Built on by (most cited first, 10 of 10)

- [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)
- [Innovative Thinking, Infinite Humor: Humor Research of Large Language Models through Structured Thought Leaps](https://mlsanity.com/paper/2410.10370/) (2024)
- [A Causality-aware Paradigm for Evaluating Creativity of Multimodal Large Language Models](https://mlsanity.com/paper/2501.15147/) (2025)
- [Laughing Heads: Can Transformers Detect What Makes a Sentence Funny?](https://mlsanity.com/paper/2105.09142/) (2021)
- [Getting Serious about Humor: Crafting Humor Datasets with Unfunny Large Language Models](https://mlsanity.com/paper/2403.00794/) (2024)
- [Creativity in AI: Progresses and Challenges](https://mlsanity.com/paper/2410.17218/) (2024)
- [Towards Multimodal Prediction of Spontaneous Humour: A Novel Dataset and First Results](https://mlsanity.com/paper/2209.14272/) (2022)
- [KoWit-24: A Richly Annotated Dataset of Wordplay in News Headlines](https://mlsanity.com/paper/2503.01510/) (2025)
- [Learning to Think Like a Cartoon Captionist: Incongruity-Resolution Supervision for Multimodal Humor Understanding](https://mlsanity.com/paper/2604.15210/) (2026)
- [Are Emotion and Rhetoric Neurons in LLM? Neuron Recognition and Adaptive Masking for Emotion-Rhetoric Prediction Steering](https://mlsanity.com/paper/2604.17255/) (2026)

## Abstract (arXiv)

Building datasets of creative text, such as humor, is quite challenging. We introduce FunLines, a competitive game where players edit news headlines to make them funny, and where they rate the funniness of headlines edited by others. FunLines makes the humor generation process fun, interactive, collaborative, rewarding and educational, keeping players engaged and providing humor data at a very low cost compared to traditional crowdsourcing approaches. FunLines offers useful performance feedback, assisting players in getting better over time at generating and assessing humor, as our analysis shows. This helps to further increase the quality of the generated dataset. We show the effectiveness of this data by training humor classification models that outperform a previous benchmark, and we release this dataset to the public.