  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.
