Humor preferences vary widely across individuals and cultures, complicating the evaluation of humor using large language models (LLMs). In this study, we model heterogeneity in humor preferences in Oogiri, a Japanese creative response game, by clustering users with voting logs and estimating cluster-specific weights over interpretable preference factors using Bradley-Terry-Luce models. We elicit preference judgments from LLMs by prompting them to select the funnier response and found that user clusters exhibit distinct preference patterns and that the LLM results can resemble those of particular clusters. Finally, we demonstrate that, by persona prompting, LLM preferences can be directed toward a specific cluster. The scripts for data collection and analysis will be released to support reproducibility.

翻译：幽默偏好因个体与文化差异而显著不同，这使得利用大型语言模型（LLMs）评估幽默变得复杂。在本研究中，我们通过聚类具有投票记录的用户，并利用Bradley-Terry-Luce模型估计可解释偏好因素上的群体特定权重，从而对日本创意应答游戏Oogiri中的幽默偏好异质性进行建模。我们通过提示LLMs选择更有趣的应答来获取其偏好判断，发现用户群体展现出独特的偏好模式，且LLM的结果可能与特定群体的结果相似。最后，我们证明通过角色提示，可以将LLM的偏好引导至特定群体。数据收集与分析脚本将被公开以支持可复现性。