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As short-form funny videos on social networks are gaining popularity, itbecomes demanding for AI models to understand them for better communicationwith humans. Unfortunately, previous video humor datasets target specificdomains, such as speeches or sitcoms, and mostly focus on verbal cues. Wecurate a user-generated dataset of 10K multimodal funny videos from YouTube,called ExFunTube. Using a video filtering pipeline with GPT-3.5, we verify bothverbal and visual elements contributing to humor. After filtering, we annotateeach video with timestamps and text explanations for funny moments. OurExFunTube is unique over existing datasets in that our videos cover a widerange of domains with various types of humor that necessitate a multimodalunderstanding of the content. Also, we develop a zero-shot video-to-textprompting 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 ourprompting significantly improves LLMs' ability for humor explanation.

Can Language Models Laugh at YouTube Short-form Videos?