CinePile presents a unique dataset and benchmark designed specifically for long-form video understanding. This research utilizes advanced large language models (LLMs) with human-in-the-loop techniques to create a comprehensive question-answer setup. Noteworthy findings highlight the current challenges faced by video-centric LLMs in matching human performance, underlining the complexity within this domain. - The dataset comprises over 305,000 MCQs covering various aspects of multimodal understanding. - It’s tested on open-source and proprietary video-centric LLMs. - Reveals a significant gap between AI and human performance in video understanding tasks. - Available publicly for research and advancement at HF Dataset.

Importance: This benchmark sets a new standard for video understanding and challenges the AI research community to develop more capable models. It opens up new avenues for enhancing AI’s capability in multimedia environments, critical for applications ranging from education to surveillance.