构建幽默理解新数据对象，提升AI解释幽默的准确性
Re-defining Humor Data Objects for AI Humor Research
- 提出幽默推理数据对象，融合上下文与解释
- 优化提示词后显著减少遗漏上下文等错误
- 可扩展生成大量数据，助力幽默研究数据增强
现有AI幽默研究多将幽默视为二元存在状态。本文将幽默重新定义为依赖上下文的社会互动，并构建了幽默推理数据对象，设计有效提示词引导大模型生成对大众有解释力的幽默说明。通过迭代优化提示词，发现改进版显著降低关键错误，成功规模化生成大量数据对象，具备支持数据合成与增强的潜力。核心结论是：精心设计的提示词能有效提升幽默解释质量，尤其在处理缺失上下文、多模态信息和文本转录问题上表现更优。该工作为未来人工智能理解幽默作为社会行为奠定了坚实基础。代码与数据已开源：https://github.com/anna-arnett/ai-humor/
原文摘要 · Abstract (English)
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/ .
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