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/ .

翻译：在现有的大多数AI幽默研究中，幽默被简单地视为“存在”或“不存在”的二元属性。我们探索了幽默作为带有上下文和解释的社会互动这一概念。在本项目中，我们定义了一种幽默推理数据对象，并开发了一种方法，通过提示大语言模型生成对普通人群有效的幽默解释。我们从早期的提示方案迭代至改进版本，发现后者显著减少了关键性错误，随后将生成规模扩展至大量数据对象，这些数据对象有望为AI幽默研究提供数据合成与数据增强支持。我们的主要结论是：优化大语言模型的提示策略能显著提升幽默解释质量，特别是在更谨慎地处理上下文缺失、多模态及转录问题方面。这些结果为未来将幽默理解视为社会行为的AI研究奠定了坚实基础。所有代码与数据均可在以下链接获取：https://github.com/anna-arnett/ai-humor/