Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges

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. This survey focuses on visual humor understanding in single-image and multi-panel artifacts, while treating humor generation as an emerging downstream frontier. We position the literature against prior humor, sarcasm, and general MLLM surveys and organize it using a capability-centric hierarchy spanning recognition, interpretation and reasoning, and generation. Under this lens, we synthesize benchmark design, evaluation protocols, and modeling paradigms, tracing the field's shift from task-specific fusion models to large-model approaches based on multimodal alignment, evidence-grounded reasoning, and controlled generation. We conclude by highlighting the main barriers to progress: shortcut-prone evaluation, limited cultural and narrative coverage, weak evidence grounding, and unresolved safety and ownership concerns.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Multimedia (cs.MM)

arXiv:2607.19011 [cs.CL]

(or arXiv:2607.19011v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.19011 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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