# Re-defining Humor Data Objects for AI Humor Research

arXiv: https://arxiv.org/abs/2605.25171 · HTML: https://mlsanity.com/paper/2605.25171/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 24 May 2026.

## Builds on (most cited first, 3 of 3)

- [Pragmatic Metacognitive Prompting Improves LLM Performance on Sarcasm Detection](https://mlsanity.com/paper/2412.04509/) (2024)
- [Text Is Not All You Need: Multimodal Prompting Helps LLMs Understand Humor](https://mlsanity.com/paper/2412.05315/) (2024)
- [The Theater Stage as Laboratory: Review of Real-Time Comedy LLM Systems for Live Performance](https://mlsanity.com/paper/2501.08474/) (2025)

## Abstract (arXiv)

In most existing AI humor research, humor was treated as either "present" or "not present." We explore the concept of humor as a social interaction with context and explanations. During this project, we defined a humor reasoning data object and developed a way to prompt LLMs to generate an explanation of humor effective for general population. We iterated from an earlier prompt to an improved prompt, found that the later version reduced important errors, and then scaled generation to a large number of data objects which have the potential to enable data synthesis and data augmentation for AI humor research. Our main takeaway is that better prompting of an LLM improves humor explanation quality, especially by handling missing context, multi-modality, and transcript issues more carefully. These results establish a strong foundation for future work on AI understanding of humor as social behavior. All code and data are available at: https://github.com/anna-arnett/ai-humor/ .