arXiv:2605.25171v1 Announce Type: new Abstract: 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 enab
The increasing sophistication of LLMs is pushing researchers to explore more nuanced and human-like interactions, making the objective understanding and generation of complex social constructs like humor critical.
Improving AI's ability to understand and generate humor moves it closer to human-level communication and social interaction, which is a key barrier for broad adoption of advanced AI systems.
The definition of 'humor' in AI research expands from a binary present/absent state to a more complex, context-rich data object, enabling more sophisticated and explicable AI humor generation.
- · AI researchers
- · LLM developers
- · Content creators using AI
- · Entertainment industry
- · AI systems with simplistic humor models
- · Early humor datasets
AI models will become better at understanding and generating humor relevant to various social contexts.
This improved humor capability could lead to more engaging and 'human-like' AI agents in conversational systems and creative applications.
The ability of AI to generate contextually appropriate humor might blur the lines further between human and AI-generated content, impacting social dynamics and ethical considerations.
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