Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges
Summary
This survey examines computational humor understanding in multimodal LLMs, covering methods, datasets, evaluation protocols, and challenges such as shortcut-prone evaluation and weak evidence grounding.
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Paper page - Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges
Source: https://huggingface.co/papers/2607.19011
Abstract
Multimodalhumorinmemes,cartoons,andcomicsremainsdifficultforAIsystemsbecauseintendedmeaningdependsonnon-literalmechanisms,sharedculturalknowledge,andcommunicativeintentratherthanliteralscenedescription.Thissurveyfocusesonvisualhumorunderstandinginsingle-imageandmulti-panelartifacts,whiletreatinghumorgenerationasanemergingdownstreamfrontier.Wepositiontheliteratureagainstpriorhumor,sarcasm,andgeneralMLLMsurveysandorganizeitusingacapability-centrichierarchyspanningrecognition,interpretationandreasoning,andgeneration.Underthislens,wesynthesizebenchmarkdesign,evaluationprotocols,andmodelingparadigms,tracingthefield’sshiftfromtask-specificfusionmodelstolarge-modelapproachesbasedonmultimodalalignment,evidence-groundedreasoning,andcontrolledgeneration.Weconcludebyhighlightingthemainbarrierstoprogress:shortcut-proneevaluation,limitedculturalandnarrativecoverage,weakevidencegrounding,andunresolvedsafetyandownershipconcerns.
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