CinePile 2.0：利用對抗性精煉打造更強大的長影片問答資料集
Original: CinePile 2.0 - making stronger datasets with adversarial refinement
CinePile is a multimodal question-answering dataset focused on movie and long-video understanding. In traditional dataset construction…
CinePile 2.0 是一個專為長影片理解設計的問答資料集更新版本。本次更新引入了「對抗性精煉（Adversarial Refinement）」技術，旨在解決 LLM 生成干擾項過於簡單或存在偏誤的問題。透過篩選掉不需看影片就能回答的漏洞題目，CinePile 2.0 能更精準地評估多模態模型對複雜視覺與敘事邏輯的真實理解能力。
CinePile is a multimodal question-answering dataset focused on movie and long-video understanding. In traditional dataset construction, researchers commonly use large language models (LLMs) to automatically generate questions, correct answers, and distractors (incorrect answer choices). However, this automated approach is prone to "shortcut learning" problems — where the test model can guess the correct answer based solely on linguistic biases, common sense, or contextual logic, without watching the video at all.
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