AdShot: Benchmarking Multimodal Large Language Models for Video Advertisement Clipping

Advertisers routinely create multiple duration variants of ads to meet marketing budgets and viewer preferences, a labor-intensive and costly process. While multimodal large language models (MLLMs) excel at general video understanding, their ability to perform specialized editorial tasks remains unexplored. We introduce \textbf{AdShot}, a benchmark for evaluating MLLMs on shot selection for ad clipping. It contains 823 professionally edited ad pairs (30-sec sources and 15-sec edits) across 194 brands and 17 industries, annotated with shot-level boundaries and content features (action, emotion, information, imagery). We evaluate 13 state-of-the-art video-only MLLMs and 2 audio-visual MLLMs (7B–72B parameters) on shot selection accuracy (Precision, Recall, F1), duration constraints (15s), and content preservation. We find: (1) model architecture matters more than scale, with smaller models often matching or exceeding larger ones; performance is strongest for target ads with 6–10 shots; (2) models exhibit a mean absolute duration error of 5.39s, with most tending to overshoot the 15s target, though they can still substantially reduce human editing effort; (3) all models exhibit systematic content biases, favoring information-rich and emotion-focused shots while under-selecting action and imagery. We publicly release our benchmark to facilitate research at the intersection of multimodal AI and computational advertising.