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: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: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)...
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: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.
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.
arXiv:2607. 13189v1 Announce Type: cross Abstract: We present RAGthoven, our system for SemEval-2026 Task 1 (MWAHAHA), Subtask A (multilingual constrained humor generation in English, Spanish, and Chinese).
arXiv:2607. 19011v1 Announce Type: cross Abstract: Multimodal humor in memes, cartoons, and comics remains difficult for AI systems because intended meaning depends on non-literal mechanisms, shared cultural knowledge, and communicative intent rather than literal scene description.
MultiHuSE is a multimodal dataset featuring 2,407 high‑definition videos of 50 diverse actors delivering 1,463 text samples in four psychological humour styles—affiliative, aggressive, self‑enhancing, and self‑deprecating—plus neutral content. Each text is performed by multiple actors, allowing analysis of expressive diversity, and a subset includes emotion annotations. Baseline experiments show that multimodal fusion improves humour style classification accuracy over unimodal approaches, especially for affiliative humour.
arXiv:2607. 15442v1 Announce Type: new Abstract: Internet memes intertwine visual cues, textual content, and cultural context, making them particularly challenging to interpret in scenarios where humor, sarcasm, and harmful intent coexist.
The paper introduces a method to compare human and large language model (LLM) outputs in the context of humorous improvisational dialogue. Using a multi‑agent environment, the authors analyze both automated linguistic metrics and manual annotations based on Systemic Functional Linguistics. Findings reveal that while LLMs can produce coherent and creative scenes, they often lack the spontaneity of human interaction and tend to be overly polite, unlike the varied attitudes seen in human comedy.
arXiv:2509. 10847v3 Announce Type: replace-cross Abstract: As artificial intelligence (AI) companions become capable of human-like communication, including telling jokes, understanding how people cognitively and affectively respond to AI-attributed humor becomes increasingly important.