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Assessing the Capabilities of LLMs in Humor:A Multi-dimensional Analysis of Oogiri Generation and Evaluation

arXiv — cs.CL•Thursday, November 13, 2025 at 5:00:00 AM
NeutralArtificial Intelligence
The recent publication on arXiv explores the humor capabilities of Large Language Models (LLMs) through a comprehensive analysis of Oogiri, a form of Japanese improvisational comedy. This study addresses a significant gap in previous research that typically relied on simplistic evaluations of humor. By expanding existing Oogiri datasets and employing a six-dimensional evaluation framework, the researchers assessed LLMs' performance in generating and evaluating humor. The results revealed that while LLMs can produce creative responses, their performance is generally between low- and mid-tier human levels, with a notable deficit in empathy. This lack of empathy is critical, as it affects the models' ability to resonate with human humor, underscoring the importance of a multifaceted approach to humor in natural language processing applications.
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