Survey maps multimodal LLMs’ weak spot: understanding memes and comics
Tuo Liang · hf · 2026-07-22
A new survey, Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges, reviews why memes, cartoons, and comics remain difficult for AI: the meaning often depends on non-literal inference, shared culture, and communicative intent.
Main takeaways
- The paper focuses on visual humor understanding in single-image and multi-panel content, with humor generation treated as an emerging downstream task.
- It organizes the field into a capability hierarchy: recognition → interpretation and reasoning → generation.
- It traces the shift from task-specific fusion models to large-model approaches built on multimodal alignment, evidence-grounded reasoning, and controlled generation.
- The survey also calls out current bottlenecks: shortcut-prone evaluation, limited cultural and narrative coverage, weak evidence grounding, and unresolved safety / ownership concerns.
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