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

arXiv:2403.00794v1 Announce Type: cross
Abstract: 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 …

abstract advances arxiv cognition cs.ai cs.cl cs.lg datasets detection facet human humor language language models language processing large language large language models natural natural language natural language processing processing type work