Who Laughs with Whom? Disentangling Influential Factors in Humor Preferences across User Clusters and LLMs
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866915711181389824 |
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| author | Murakami, Soichiro Kamigaito, Hidetaka Takamura, Hiroya Okumura, Manabu |
| author_facet | Murakami, Soichiro Kamigaito, Hidetaka Takamura, Hiroya Okumura, Manabu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03103 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Who Laughs with Whom? Disentangling Influential Factors in Humor Preferences across User Clusters and LLMs Murakami, Soichiro Kamigaito, Hidetaka Takamura, Hiroya Okumura, Manabu Computation and Language Artificial Intelligence 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. |
| title | Who Laughs with Whom? Disentangling Influential Factors in Humor Preferences across User Clusters and LLMs |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2601.03103 |