mlsanityData to 14 Oct 2026

Prompt to GPT-3: Step-by-Step Thinking Instructions for Humor Generation

arXiv 2306.13195 ↗First posted 22 Jun 2023cs.CL cs.AIcited by 5

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Abstract (arXiv)

Artificial intelligence has made significant progress in natural language processing, with models like GPT-3 demonstrating impressive capabilities. However, these models still have limitations when it comes to complex tasks that require an understanding of the user, such as mastering human comedy writing strategies. This paper explores humor generation using GPT-3 by modeling human comedy writing theory and leveraging step-by-step thinking instructions. In addition, we explore the role of cognitive distance in creating humor.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 6 of its 9 references that are in the map. First posted on arXiv 22 Jun 2023.

Builds on

6 of 9 references.

References · 6

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  3. 2019Pun Generation with Surprisecited by 37
  4. 2020Let's be Humorous: Knowledge Enhanced Humor Generationcited by 10
  5. 2023Witscript 3: A Hybrid AI System for Improvising Jokes in a Conversationcited by 8
  6. 2017Is This a Joke? Detecting Humor in Spanish Tweetscited by 4

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Cited by year

Citing papers per year: 2024: 2; 2025: 3.

5 papers.

  1. 2024A Robot Walks into a Bar: Can Language Models Serve as Creativity Support Tools for Comedy? An Evaluation of LLMs' Humour Alignment with Comedianscited by 29
  2. 2024Humor Mechanics: Advancing Humor Generation with Multistep Reasoningcited by 8
  3. 2025The Theater Stage as Laboratory: Review of Real-Time Comedy LLM Systems for Live Performancecited by 4
  4. 2025Engagement Undermines Safety: How Stereotypes and Toxicity Shape Humor in Language Modelscited by 1
  5. 2025Psychology-Driven Enhancement of Humour Translationcited by 0