  Large language models such as GPT-3 have demonstrated an impressive
capability to adapt to new tasks without requiring task-specific training data.
This capability has been particularly effective in settings such as narrative
question answering, where the diversity of tasks is immense, but the available
supervision data is small. In this work, we investigate if such language models
can extend their zero-shot reasoning abilities to long multimodal narratives in
multimedia content such as drama, movies, and animation, where the story plays
an essential role. We propose Long Story Short, a framework for narrative video
QA that first summarizes the narrative of the video to a short plot and then
searches parts of the video relevant to the question. We also propose to
enhance visual matching with CLIPCheck. Our model outperforms state-of-the-art
supervised models by a large margin, highlighting the potential of zero-shot QA
for long videos.

Computer Vision and Pattern Recognition;Artificial Intelligence