# The Theater Stage as Laboratory: Review of Real-Time Comedy LLM Systems for Live Performance

arXiv: https://arxiv.org/abs/2501.08474 · HTML: https://mlsanity.com/paper/2501.08474/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 55 references that are in the map.
- First posted on arXiv 14 Jan 2025.

## Builds on (most cited first, 13 of 13)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [A Neural Conversational Model](https://mlsanity.com/paper/1506.05869/) (2015)
- [Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionals](https://mlsanity.com/paper/2209.14958/) (2022)
- [The Perils of Using Mechanical Turk to Evaluate Open-Ended Text Generation](https://mlsanity.com/paper/2109.06835/) (2021)
- [A Robot Walks into a Bar: Can Language Models Serve as Creativity Support Tools for Comedy? An Evaluation of LLMs' Humour Alignment with Comedians](https://mlsanity.com/paper/2405.20956/) (2024)
- [Copyright Protection and Accountability of Generative AI:Attack, Watermarking and Attribution](https://mlsanity.com/paper/2303.09272/) (2023)
- [Collaborative Storytelling with Human Actors and AI Narrators](https://mlsanity.com/paper/2109.14728/) (2021)
- [Automatically Generating Engaging Presentation Slide Decks](https://mlsanity.com/paper/1903.09308/) (2019)
- [Witscript 3: A Hybrid AI System for Improvising Jokes in a Conversation](https://mlsanity.com/paper/2301.02695/) (2023)
- [Improbotics: Exploring the Imitation Game using Machine Intelligence in Improvised Theatre](https://mlsanity.com/paper/1809.01807/) (2018)
- [Prompt to GPT-3: Step-by-Step Thinking Instructions for Humor Generation](https://mlsanity.com/paper/2306.13195/) (2023)
- [Improvised Comedy as a Turing Test](https://mlsanity.com/paper/1711.08819/) (2017)
- [Designing and Evaluating Dialogue LLMs for Co-Creative Improvised Theatre](https://mlsanity.com/paper/2405.07111/) (2024)

## Built on by (most cited first, 4 of 4)

- [Multi-Agent Comedy Club: Investigating Community Discussion Effects on LLM Humor Generation](https://mlsanity.com/paper/2602.14770/) (2026)
- [Timing In stand-up Comedy: Text, Audio, Laughter, Kinesics (TIC-TALK): Pipeline and Database for the Multimodal Study of Comedic Timing](https://mlsanity.com/paper/2603.21803/) (2026)
- ["Not Human, Funnier": How Machine Identity Shapes Humor Perception in Online AI Stand-up Comedy](https://mlsanity.com/paper/2602.12763/) (2026)
- [Re-defining Humor Data Objects for AI Humor Research](https://mlsanity.com/paper/2605.25171/) (2026)

## Abstract (arXiv)

In this position paper, we review the eclectic recent history of academic and artistic works involving computational systems for humor generation, and focus specifically on live performance. We make the case that AI comedy should be evaluated in live conditions, in front of audiences sharing either physical or online spaces, and under real-time constraints. We further suggest that improvised comedy is therefore the perfect substrate for deploying and assessing computational humor systems. Using examples of successful AI-infused shows, we demonstrate that live performance raises three sets of challenges for computational humor generation: 1) questions around robotic embodiment, anthropomorphism and competition between humans and machines, 2) questions around comedic timing and the nature of audience interaction, and 3) questions about the human interpretation of seemingly absurd AI-generated humor. We argue that these questions impact the choice of methodologies for evaluating computational humor, as any such method needs to work around the constraints of live audiences and performance spaces. These interrogations also highlight different types of collaborative relationship of human comedians towards AI tools.