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This paper presents the Crowd Score, a novel method to assess the funninessof jokes using large language models (LLMs) as AI judges. Our method relies oninducing different personalities into the LLM and aggregating the votes of theAI judges into a single score to rate jokes. We validate the votes using anauditing technique that checks if the explanation for a particular vote isreasonable using the LLM. We tested our methodology on 52 jokes in a crowd offour AI voters with different humour types: affiliative, self-enhancing,aggressive and self-defeating. Our results show that few-shot prompting leadsto better results than zero-shot for the voting question. Personality inductionshowed that aggressive and self-defeating voters are significantly moreinclined to find more jokes funny of a set of aggressive/self-defeating jokesthan the affiliative and self-enhancing voters. The Crowd Score follows thesame trend as human judges by assigning higher scores to jokes that are alsoconsidered funnier by human judges. We believe that our methodology could beapplied to other creative domains such as story, poetry, slogans, etc. It couldboth help the adoption of a flexible and accurate standard approach to comparedifferent work in the CC community under a common metric and by minimizinghuman participation in assessing creative artefacts, it could accelerate theprototyping of creative artefacts and reduce the cost of hiring humanparticipants to rate creative artefacts.

Crowd Score: A Method for the Evaluation of Jokes using Large Language Model AI Voters as Judges