Multi-Agent Comedy Club: Investigating Community Discussion Effects on LLM Humor Generation
AI 摘要
研究社区讨论如何提升LLM生成的喜剧文本质量,显著提升了可读性和社会回应。
主要贡献
- 提出利用社区讨论提升LLM喜剧生成质量的方法
- 建立了多智能体喜剧俱乐部环境进行受控实验
- 证明了社区讨论能显著提升喜剧文本的质量
方法论
构建多智能体环境,模拟社区讨论,将讨论记录作为社会记忆,用于后续生成,对比有无讨论的生成效果。
原文摘要
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 (Δ = 0.440) and Social Response (Δ = 0.422), with occasional increases in aggressive humor.