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LLMs mimic human humor preferences in Japanese Oogiri game

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method to understand humor preferences by clustering users and analyzing their responses in a Japanese creative game called Oogiri. They employed Bradley-Terry-Luce models to identify distinct preference patterns within these user clusters. The study also found that large language models (LLMs) can exhibit preferences similar to specific user clusters, and their humor judgments can be influenced through persona prompting. AI

IMPACT This research offers a novel approach to evaluating LLM capabilities in understanding nuanced human traits like humor.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new methodology for analyzing humor preferences. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs mimic human humor preferences in Japanese Oogiri game

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The cluster contains a research paper published on arXiv detailing a new methodology for analyzing humor preferences. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Soichiro Murakami, Hidetaka Kamigaito, Hiroya Takamura, Manabu Okumura ·

    Who Laughs with Whom? Disentangling Influential Factors in Humor Preferences across User Clusters and LLMs

    arXiv:2601.03103v2 Announce Type: replace-cross Abstract: 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 creat…

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