United States

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 &quot;topic handles&quot; – the key nouns or short noun phrases that capture a joke&#39;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.

ACL 2026

Navigating the Joke Space: Towards Automated Originality Assessment of AI-Generated Humor

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.

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 Divergence Hypothesis: Unmasking Lexical Interference and Label Bias in Mental Health NLP

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