Humor Mechanics: Advancing Humor Generation with Multistep Reasoning

TL;DR

This paper introduces a multi-step reasoning approach to generate humorous one-liner jokes, demonstrating improved quality over existing methods through human evaluations and experimental validation.

Contribution

It presents a novel multi-step reasoning framework for humor generation and provides empirical evidence of its effectiveness compared to baseline models.

Findings

Multi-step reasoning enhances humor quality.

The approach outperforms zero-shot GPT-4 and baselines.

Human evaluations confirm improved humor generation.

Abstract

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.