Re-defining Humor Data Objects for AI Humor Research

2026-06-08 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

This research redefines humor data objects for AI, moving beyond binary "present/not present" labels to treat humor as a social interaction. Researchers defined a "humor reasoning" data object, a quintuple (C,X;Y,R;Z) encompassing context, humorous attempt, receiver reaction, explanation, and recovery. They developed and refined LLM prompts, using gpt-4o-mini, to generate structured humor explanations (R). An improved prompt significantly reduced errors from 14 unacceptable explanations out of 31 to just 4, primarily by better addressing missing context, multimodal dependencies, and transcript quality issues. This refined method was then scaled to generate 307 explanation examples, establishing a foundation for data synthesis and augmentation in AI humor research.

Key takeaway

For NLP Engineers developing AI systems for humor understanding, recognize that humor is a complex social behavior requiring more than binary labels. You should prioritize defining structured data objects that capture context, reactions, and explanations. Invest in iterative prompt engineering, informed by manual error analysis, to generate high-quality reasoning data from LLMs, especially when source transcripts have missing context or multimodal dependencies. This approach significantly improves data reliability and model development.

Key insights

AI humor research improves by defining humor as a social interaction with context and explanations, not just a binary state.

Principles

• Humor is a social behavior, not a binary event.
• Prompt design significantly impacts LLM explanation quality.
• Manual error analysis is essential for prompt refinement.

Method

Develop LLM prompts to generate structured humor explanations, including reasoning steps and data quality flags, based on context, attempt, and reaction, iterating prompts via manual error analysis.

In practice

• Adopt a quintuple data object for humor research.
• Refine LLM prompts by analyzing specific failure categories.
• Incorporate data quality flags for multimodal or incomplete transcripts.

Topics

• Computational Humor
• Large Language Models
• Prompt Engineering
• Humor Reasoning
• Data Augmentation
• Multimodal AI

Best for: AI Scientist, NLP Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.