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).
By Marek \v{S}uppa, Vikt\'oria Ondrejov\'a, Lucia Ganajov\'a, Gregor Karetka, Daniel Skala
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.
By Sydney Johns, Sanjeev Parthasarathy, Shantnu Bhalla, Vaibhav Garg
arXiv:2610.28337v1 Announce Type: cross
Abstract: Emoji are widely treated as compact affective tokens whose meaning is summarised by a single sentiment score. We argue that this picture is incomplet...
By Rohan James, Ioannis Christofilogiannis, Yuuv Jauhari, Shreya Shah, Gauri Pethkar
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.
By Alexey Tikhonov, Alexey Ivanov
arXiv:2507. 06506v2 Announce Type: replace-cross Abstract: Translating wordplay across languages presents unique challenges that have long confounded both professional human translators and machine translation systems.
By Russell Taylor, Benjamin Herbert, Michael Sana
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.
By Shanhong Liu, Pai Chet Ng, De Wen Soh, Malika Meghjani, Konstantinos N. Plataniotis
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)...
By Edward Ajayi, Prasenjit Mitra
arXiv:2601.09041v2 Announce Type: replace-cross
Abstract: Large language models (LLMs) often produce judgments similar to those of humans, but their interpretation of figurative language remains uncl...
By Samhita Bollepally, Aurora Sloman-Moll, Takashi Yamauchi, Neeka Motamedrasa
arXiv:2608.23172v1 Announce Type: new
Abstract: Large-scale vision-language models (VLMs) have demonstrated remarkable versatility across a wide range of multimodal tasks. However, understanding humo...
By Abhilash Nandy, Rahul Seetharaman, Aman Bansal, Rounak Saha, Manav Nitin Kapadnis, Millon Madhur Das, Pawan Goyal, Niloy Ganguly
arXiv:2603.04317v2 Announce Type: replace-cross
Abstract: A growing literature shows that variables can be linearly decoded from the activations of large language models (LLMs). These range from prop...
By Elan Barenholtz
arXiv:2601.03103v2 Announce Type: replace-cross
Abstract: Humor preferences vary widely across individuals and cultures, complicating the evaluation of humor using large language models (LLMs). In th...
By Soichiro Murakami, Hidetaka Kamigaito, Hiroya Takamura, Manabu Okumura
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.
By Tuo Liang, Zhe Hu, Disheng Liu, Jing Li, Yu Yin