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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.

ACL 2026

Cards Against LLMs: Benchmarking Humor Alignment in Large Language Models

workshop paper

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

We present exploratory experiments in the comedic roasting capabilities of GPT4o. Specifically, @ComedyCentral roasts were scraped to design a survey in which participants blindly evaluated snippets of human and AI roasts, and had to predict the author (AI/human) in a second round of reviewing. The results show that there is no significant difference in how the barbs in human- and AI-generated roasts are rated. Further, a qualitative analysis showed that although the model utilizes specific recurrent phrases to imitate the style of human comedians, both generative LLM detectors and humans performed suboptimally in predicting the true author of the roasts.

The Roast of GPT4o: Experiments in Generating, Detecting and Evaluating Celebrity Roast Comedy

We investigate whether scaling model parameters improves humor generation through a controlled ablation study. Using five Qwen3 variants (8B–235B, dense and MoE), we generate jokes across 50 themes. Beyond evaluating humor scaling, this work serves as an empirical study into the nature of LLM versus human evaluations on highly subjective creative tasks. While an automated judge yields a perfect monotonic ranking between parameter count and win rate, human annotators find no significant aggregate difference in humor quality. Restricting to themes where annotators agree reveals a significant preference for the largest model (p = 0.039), suggesting scaling effects exist but are masked by a "quality floor." Crucially, our analysis of bias characteristics shows that the automated judge exhibits severe positional and length biases compared to human evaluators, further suggesting that LLMs may systematically distort quality differences on subjective tasks.

Does Bigger Mean Funnier? Evaluating Humor Generation Across the Qwen3 Model Family

Large language models (LLMs) can generate jokes that rival human comedy in quality, yet their originality remains uncertain. This study develops and validates automated, corpus-based methods for quantifying joke originality by focusing on core "topic handles" – the key nouns or short noun phrases that capture a joke's script opposition and logical mechanism (per the General Theory of Verbal Humor). Using a reference corpus of one million English jokes from Reddit, we compute Pointwise Mutual Information (PMI) on handle pairs in three variants (raw co-occurrence, semantic-cluster smoothing, and word- decomposition) and two embedding-based measures (handle-level conceptual distance and full-text corpus novelty via Sentence-BERT). We tested the measures on 400 LLM- generated jokes (200 from GPT-4o and 200 from GPT-5.4 prompted for original two-sentence jokes) and 80 benchmark jokes from the Witscript-powered JEST benchmark (including non- humorous controls). Three professional comedians rated all items on 0–3 scales of originality and funniness. Automated measures, especially raw PMI average (ρ = .386) and corpus novelty (ρ = .372), correlated significantly with human originality ratings but showed no relation to funniness. Benchmark jokes were rated most original, and the originality– funniness relationship differed sharply across sources (positive for benchmark, negative for GPT-4o). The results demonstrate that handle-based PMI and semantic novelty metrics offer practical, quantitative tools for assessing originality in both human and AI-generated humor, advancing objective evaluation of computational creativity while highlighting distinct patterns in how general-purpose LLMs versus specialized humor systems produce novel jokes.

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The Roast of GPT4o: Experiments in Generating, Detecting and Evaluating Celebrity Roast Comedy

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Next from ACL 2026

The Roast of GPT4o: Experiments in Generating, Detecting and Evaluating Celebrity Roast Comedy

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

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