# "Judge me by my size (noun), do you?'' YodaLib: A Demographic-Aware Humor Generation Framework

arXiv: https://arxiv.org/abs/2006.00578 · HTML: https://mlsanity.com/paper/2006.00578/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2020.

## Built on by (most cited first, 8 of 8)

- [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)
- [The Iron(ic) Melting Pot: Reviewing Human Evaluation in Humour, Irony and Sarcasm Generation](https://mlsanity.com/paper/2311.05552/) (2023)
- [M2H2: A Multimodal Multiparty Hindi Dataset For Humor Recognition in Conversations](https://mlsanity.com/paper/2108.01260/) (2021)
- [Comparing Apples to Oranges: A Dataset & Analysis of LLM Humour Understanding from Traditional Puns to Topical Jokes](https://mlsanity.com/paper/2507.13335/) (2025)
- [Cards Against AI: Predicting Humor in a Fill-in-the-blank Party Game](https://mlsanity.com/paper/2210.13016/) (2022)
- [Context-Situated Pun Generation](https://mlsanity.com/paper/2210.13522/) (2022)
- [Who's Laughing Now? An Overview of Computational Humour Generation and Explanation](https://mlsanity.com/paper/2509.21175/) (2025)

## Abstract (arXiv)

The subjective nature of humor makes computerized humor generation a challenging task. We propose an automatic humor generation framework for filling the blanks in Mad Libs stories, while accounting for the demographic backgrounds of the desired audience. We collect a dataset consisting of such stories, which are filled in and judged by carefully selected workers on Amazon Mechanical Turk. We build upon the BERT platform to predict location-biased word fillings in incomplete sentences, and we fine tune BERT to classify location-specific humor in a sentence. We leverage these components to produce YodaLib, a fully-automated Mad Libs style humor generation framework, which selects and ranks appropriate candidate words and sentences in order to generate a coherent and funny story tailored to certain demographics. Our experimental results indicate that YodaLib outperforms a previous semi-automated approach proposed for this task, while also surpassing human annotators in both qualitative and quantitative analyses.