Re-defining Humor Data Objects for AI Humor Research
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 qu...
| Authors | Anna Arnett, Bang Nguyen, Meng Jiang |
|---|---|
| Primary category | cs.CL |
| Published | 24 May 2026 |
| Last updated at source | 5 June 2026 |
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