In this paper, we explore the generation of one-liner jokes through multi-step reasoning. Our work involved reconstructing the process behind creating humorous one-liners and developing a working prototype for humor generation. We conducted comprehensive experiments with human participants to evaluate our approach, comparing it with human-created jokes, zero-shot GPT-4 generated humor, and other baselines. The evaluation focused on the quality of humor produced, using human labeling as a benchmark. Our findings demonstrate that the multi-step reasoning approach consistently improves the quality of generated humor. We present the results and share the datasets used in our experiments, offering insights into enhancing humor generation with artificial intelligence.

翻译：本文探讨了通过多步推理生成单行笑话的方法。我们的工作重构了幽默单行创意背后的过程，并开发了一个幽默生成的工作原型。我们进行了全面的人机实验来评估该方法，将其与人工创作的笑话、零样本GPT-4生成的幽默及其他基线进行了比较。评估聚焦于所生成幽默的质量，并以人类标注为基准。研究结果表明，多步推理方法能持续提升生成幽默的质量。我们展示了实验结果，并公开了实验中使用的数据集，为利用人工智能增强幽默生成提供了见解。