  As short-form funny videos on social networks are gaining popularity, it
becomes demanding for AI models to understand them for better communication
with humans. Unfortunately, previous video humor datasets target specific
domains, such as speeches or sitcoms, and mostly focus on verbal cues. We
curate a user-generated dataset of 10K multimodal funny videos from YouTube,
called ExFunTube. Using a video filtering pipeline with GPT-3.5, we verify both
verbal and visual elements contributing to humor. After filtering, we annotate
each video with timestamps and text explanations for funny moments. Our
ExFunTube is unique over existing datasets in that our videos cover a wide
range of domains with various types of humor that necessitate a multimodal
understanding of the content. Also, we develop a zero-shot video-to-text
prompting to maximize video humor understanding of large language models
(LLMs). With three different evaluation methods using automatic scores,
rationale quality experiments, and human evaluations, we show that our
prompting significantly improves LLMs' ability for humor explanation.
