"I See What You Did There": Can Large Vision-Language Models Understand Multimodal Puns?

📰 ArXiv cs.AI

Researchers investigate whether large vision-language models can understand multimodal puns, which combine visual and textual elements to create humor

advanced Published 8 Apr 2026

Action Steps

Collect and annotate a dataset of multimodal puns with visual and textual elements
Develop and fine-tune vision-language models to understand the literal and figurative meanings of puns
Evaluate the performance of vision-language models on the dataset using metrics such as accuracy and F1-score
Analyze the results to identify the strengths and weaknesses of vision-language models in understanding multimodal puns

Who Needs to Know This

AI researchers and natural language processing engineers can benefit from this study to improve the understanding of multimodal puns in vision-language models, and apply the findings to develop more sophisticated language understanding systems

Key Insight

💡 Vision-language models can be fine-tuned to understand multimodal puns, but their performance is limited by the quality of the training data and the complexity of the puns