@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://preview.aclanthology.org/ingest-acl/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."
}