@inproceedings{hong-etal-2026-multi,
title = "Multi-Agent Comedy Club: Investigating Community Discussion Effects on {LLM} Humor Generation",
author = "Hong, Shiwei and
Li, Lingyao and
Rong, Ethan Z. and
Shen, Chenxinran and
Lu, Zhicong",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.145/",
pages = "2981--2998",
ISBN = "979-8-89176-395-1",
abstract = "Prior work has explored multi-turn interaction and feedback for LLM writing, but evaluations still largely center on prompts and localized feedback, leaving persistent public reception in online communities underexamined. We test whether broadcast community discussion improves stand-up comedy writing in a controlled multi-agent sandbox: in the discussion condition, critic and audience threads are recorded, filtered, stored as social memory, and later retrieved to condition subsequent generations, whereas the baseline omits discussion. Across 50 rounds (250 paired monologues) judged by five expert annotators using A/B preference and a 15-item rubric, discussion wins 75.6{\%} of instances and improves Craft/Clarity ({\ensuremath{\Delta}} = 0.440) and Social Response ({\ensuremath{\Delta}} = 0.422), with occasional increases in aggressive humor."
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<abstract>Prior work has explored multi-turn interaction and feedback for LLM writing, but evaluations still largely center on prompts and localized feedback, leaving persistent public reception in online communities underexamined. We test whether broadcast community discussion improves stand-up comedy writing in a controlled multi-agent sandbox: in the discussion condition, critic and audience threads are recorded, filtered, stored as social memory, and later retrieved to condition subsequent generations, whereas the baseline omits discussion. Across 50 rounds (250 paired monologues) judged by five expert annotators using A/B preference and a 15-item rubric, discussion wins 75.6% of instances and improves Craft/Clarity (\ensuremathΔ = 0.440) and Social Response (\ensuremathΔ = 0.422), with occasional increases in aggressive humor.</abstract>
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%0 Conference Proceedings
%T Multi-Agent Comedy Club: Investigating Community Discussion Effects on LLM Humor Generation
%A Hong, Shiwei
%A Li, Lingyao
%A Rong, Ethan Z.
%A Shen, Chenxinran
%A Lu, Zhicong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F hong-etal-2026-multi
%X Prior work has explored multi-turn interaction and feedback for LLM writing, but evaluations still largely center on prompts and localized feedback, leaving persistent public reception in online communities underexamined. We test whether broadcast community discussion improves stand-up comedy writing in a controlled multi-agent sandbox: in the discussion condition, critic and audience threads are recorded, filtered, stored as social memory, and later retrieved to condition subsequent generations, whereas the baseline omits discussion. Across 50 rounds (250 paired monologues) judged by five expert annotators using A/B preference and a 15-item rubric, discussion wins 75.6% of instances and improves Craft/Clarity (\ensuremathΔ = 0.440) and Social Response (\ensuremathΔ = 0.422), with occasional increases in aggressive humor.
%U https://aclanthology.org/2026.findings-acl.145/
%P 2981-2998
Markdown (Informal)
[Multi-Agent Comedy Club: Investigating Community Discussion Effects on LLM Humor Generation](https://aclanthology.org/2026.findings-acl.145/) (Hong et al., Findings 2026)
ACL