Getting Serious about Humor: Crafting Humor Datasets with Unfunny Large Language Models

Understand

Humor is a fundamental facet of human cognition and interaction.

• Yet, despite recent advances in natural language processing, humor detection remains a challenging task that is complicated by the scarcity of datasets that pair humorous texts with similar non-humorous counterparts.
• In our work, we investigate whether large language models (LLMs), can generate synthetic data for humor detection via editing texts.
• We benchmark LLMs on an existing human dataset and show that current LLMs display an impressive ability to 'unfun' jokes, as judged by humans and as measured on the downstream task of humor detection.