arXiv AI
Who Laughs with Whom? Disentangling Influential Factors in Humor Preferences across User Clusters and LLMs
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lmfaoooo at SemEval-2026 Task 1: Humor Is an Audience. Preference Modeling for Constrained Humor Generation
arXiv:2606. 00022v1 Announce Type: cross Abstract: Humor generation remains difficult not only because producing fluent, novel jokes is hard, but because "funny" is audience-dependent and supervision is noisy -- preferences vary with audience, context, and culture, and annotator agreement is often low.
arXiv AI
Jun 12
Humor Style Drives Laughter, Topic Shapes Acceptability: Evaluating Bilingual Personal and Political Robot-Delivered AI Jokes
arXiv:2606. 13256v1 Announce Type: cross Abstract: Humor plays a central role in human social relationships, and recent advances in computational humor create new opportunities for integrating humor into human-robot interaction (HRI).
arXiv Computation and Language
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HumorGen: Cognitive Synergy for Humor Generation in Large Language Models via Persona-Based Distillation
arXiv:2604.09629v3 Announce Type: replace Abstract: Humor generation poses a significant challenge for Large Language Models (LLMs), because their standard training objective (next-token prediction)...
arXiv AI
Oct 2
Comedic Fool's Gold: Reward Exploits and Countermeasures in Conversational Humor
The paper investigates automated reward systems for training language models in conversational humor, examining how reward exploits can undermine intended behavior. It evaluates two reward approaches—an embedding-based surprise reward and an audience-model laughter prediction—showing that each can be tricked by word shuffling or laughter cues, respectively. Countermeasures such as fluency filtering and cue normalization mitigate some attacks but also risk rejecting genuine witty responses, highlighting the difficulty of designing robust rewards.
arXiv AI
Jun 2
When Jokes Cross the Line: Analyzing Regular Humor and Dark Humor in YouTube Shorts
arXiv:2606. 00046v1 Announce Type: cross Abstract: Video platforms such as YouTube have reshaped how users engage with entertainment and information, emphasizing brief, highly engaging content such as Shorts.
arXiv Computation and Language
Aug 24
Jokes Aside: Measuring the Semantic Distance of Double Meanings
The paper investigates how semantic distance and ambiguity contribute to joke humor by revisiting and extending metrics from prior work. It introduces a new symmetry metric—measuring how close the ambiguous element Z is to both X and Y—and evaluates it using two embedding models on three joke datasets, including expanded versions with paired ambiguous sentences. Although models based on these metrics performed poorly in predicting humor ratings, the symmetry metric consistently correlated with higher-rated jokes, hinting it captures a key, though not sole, property of humor.