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Prompt to GPT-3: Step-by-Step Thinking Instructions for Humor Generation
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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.
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Cited by 2 Pith papers
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Psychology-Driven Enhancement of Humour Translation
A decomposition-and-recomposition prompt method for humor translation reports large gains on LLM-based metrics, but the evaluation lacks human validation and statistical checks.
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The Theater Stage as Laboratory: Review of Real-Time Comedy LLM Systems for Live Performance
A review arguing that live improvised comedy should be the central testbed for evaluating computational humor systems.
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