Stand-up and deliver: Modelling stand-up comedy to assess the self-improvement abilities of large language models

This thesis investigates the self-improvement capabilities of Large Language Models (LLMs) through the domain of stand-up comedy. With its fundamental reliance on continuous feedback and iterative refinement, stand-up presents an ideal medium for exploring whether computational systems can demonstrate genuine creative evolution. Specifically, this research focuses on the generation of long-form anecdotes as part of a cohesive narrative routine, moving beyond the traditional computational focus on short-form wordplay.

To achieve this, the study introduces a novel prompt-engineering methodology designed to guide several commercial LLMs through a simulated ''Engagement-Reflection" cognitive model. This pipeline forces the models to sequentially draft, self-critique, and iteratively improve comedy material. Throughout this process, the models demonstrate the capacity to identify structural flaws and independently apply revisions that align with established comedic mechanics, such as comedic timing, incongruity, and known techniques of the medium.

An analysis of the results reveals that clear, actionable improvements are both identified and executed by the models. By employing a triangulation evaluation strategy, the research highlights substantial quantitative and qualitative differences between the iterative drafts, showcasing measurable changes in pacing, semantic cohesion, and the underlying structure of the humour.

This thesis presents a promising framework for interacting with LLMs to reach an optimal creative result. The generated material exhibits clear ''Progression and Development'', fulfilling a key component of computational creativity, and suggests significant applications for these models within the broader creative writing industry. However, the ultimate success of the generated routines as viable stand-up material remains unclear. Determining true performative validity requires further evaluation by domain experts or through live stage performance.