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The article explores why AI chatbots struggle to generate original humor, arguing that this ability should be a benchmark for assessing Artificial General Intelligence (AGI).
This paper presents a system for constrained humor generation that uses a generate-many select-best strategy with a preference model learned from human comparisons. It achieved top ranks in English and Chinese subtasks and second in Spanish at SemEval-2026 Task 1.
HumorRank introduces a tournament-based leaderboard using pairwise evaluations and Bradley-Terry MLE to rank LLMs on humor generation, showing humor quality depends on comedic mastery rather than scale.