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.

通过多步推理，我们探讨了一句笑话的生成。通过重构创造幽默的过程并开发工作原型来生成幽默。我们进行了涉及人类参与者的全面实验，将其与人类创造的笑话、零样本 GPT-4 生成的幽默和其他基准进行比较。评估侧重于所生成幽默的质量，以人工标注作为基准。我们的发现表明，多步推理方法始终改善了所生成幽默的质量。我们呈现了结果，并分享了我们实验中使用的数据集，以提供关于如何通过人工智能提升幽默生成的见解。

幽默机制：运用多步推理推动幽默生成

Humor Mechanics: Advancing Humor Generation with Multistep Reasoning

Artificial intelligence has made significant progress in natural language
processing, with models like GPT-3 demonstrating impressive capabilities.
However, these models still have limitations when it comes to complex tasks
that require an understanding of the user, such as mastering human comedy
writing strategies. This paper explores humor generation using GPT-3 by
modeling human comedy writing theory and leveraging step-by-step thinking
instructions. In addition, we explore the role of cognitive distance in
creating humor.

该论文探讨了使用 GPT-3 模型进行幽默生成，包括建模人类喜剧写作理论和利用分步思维指导。此外，还探讨了认知距离在创造幽默方面的作用。

幽默生成的逐步思考指导

Prompt to GPT-3: Step-by-Step Thinking Instructions for Humor Generation

Building datasets of creative text, such as humor, is quite challenging. We
introduce FunLines, a competitive game where players edit news headlines to
make them funny, and where they rate the funniness of headlines edited by
others. FunLines makes the humor generation process fun, interactive,
collaborative, rewarding and educational, keeping players engaged and providing
humor data at a very low cost compared to traditional crowdsourcing approaches.
FunLines offers useful performance feedback, assisting players in getting
better over time at generating and assessing humor, as our analysis shows. This
helps to further increase the quality of the generated dataset. We show the
effectiveness of this data by training humor classification models that
outperform a previous benchmark, and we release this dataset to the public.

通过引入一款名为 FunLines 的竞争性游戏，玩家可以对新闻标题进行编辑，从而使它们更有趣。通过这个游戏，我们可以以很低的成本获得有用的幽默数据集，并通过该数据集培训幽默分类模型，从而达到优越的性能表现。