Cards Against LLMs: Benchmarking Humor Alignment in Large Language Models
On the map: LLM alignment / RLHF / preference optimisation → Human values and AI feedback alignment
Abstract (arXiv)
Humor is one of the most culturally embedded and socially significant dimensions of human communication, yet it remains largely unexplored as a dimension of Large Language Model (LLM) alignment. In this study, five frontier language models play the same Cards Against Humanity games (CAH) as human players. The models select the funniest response from a slate of ten candidate cards across 9,894 rounds. While all models exceed the random baseline, alignment with human preference remains modest. More striking is that models agree with each other substantially more often than they agree with humans. We show that this preference is partly explained by systematic position biases and content preferences, raising the question whether LLM humor judgment reflects genuine preference or structural artifacts of inference and alignment.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 8 of its 18 references that are in the map. First posted on arXiv 9 Apr 2026.
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- 2020Language Models are Few-Shot Learnerscited by 32,199
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- 2022Out of One, Many: Using Language Models to Simulate Human Samplescited by 737
- 2022Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contestcited by 98
- 2022Help me write a poem: Instruction Tuning as a Vehicle for Collaborative Poetry Writingcited by 71
- 2024Humor in AI: Massive Scale Crowd-Sourced Preferences and Benchmarks for Cartoon Captioningcited by 18
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- 2025Operationalizing Pluralistic Values in Large Language Model Alignment Reveals Trade-offs in Safety, Inclusivity, and Model Behavior
- 2026Alignment Makes Language Models Normative, Not Descriptive
- 2025Human Preferences for Constructive Interactions in Language Model Alignment
- 2025Which LLMs Get the Joke? Probing Non-STEM Reasoning Abilities with HumorBench
- 2024Dissecting Human and LLM Preferences