谁与谁一起笑？在用户集群和LLMs中解构影响幽默偏好的因素
Who Laughs with Whom? Disentangling Influential Factors in Humor Preferences across User Clusters and LLMs
AI总结：
本研究通过聚类和LLM提示解构幽默偏好影响因素，展示用户集群与LLM偏好之间的关联及可调节性。
摘要：
幽默偏好在个体和文化之间差异很大，这使得使用大语言模型（LLMs）评估幽默变得复杂。在本研究中，我们通过聚类具有投票日志的用户，并使用布拉德利-蒂尔-卢斯模型估计簇特定的权重，来建模Oogiri（一款日本创意回应游戏）中幽默偏好的异质性。我们通过提示LLMs选择更有趣的回应来获取偏好判断，并发现用户集群表现出不同的偏好模式，且LLM的结果可以模拟特定集群的结果。最后，我们证明通过角色提示，LLM的偏好可以指向特定的集群。数据收集和分析的脚本将被释放以支持可重复性。
英文摘要：
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 are publicly available to support reproducibility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
