arXiv:2510. 08543v2 Announce Type: replace-cross Abstract: As Video Large Language Models (VideoLLMs) are deployed globally, it is important to assess their ability to reason across cultural contexts.
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CultureVidBench is a new benchmark that evaluates how well text‑to‑video generation models capture cultural details. It contains 1,000 prompts spanning 12 countries, 6 continents, 8 cultural regions, and 14 cultural aspects, grouped into material culture, social practice & performance, and ritual & ceremony. Human studies and automated assessments show that while current models perform well on semantic adherence and visual quality, they often miss fine‑grained cultural details, especially for underrepresented regions and multimodal cues.
arXiv:2606. 07311v1 Announce Type: cross Abstract: As video generation models like Veo 3.
The Cultural Moment Benchmark (CMB) evaluates video cultural reasoning in Southeast Asia by testing three distinct abilities: naming a cultural concept, visually recognizing it in a video, and locating its sub‑events in time. It contains 306 expert‑curated concepts from seven countries across five categories, with each concept assessed through three stages that use semantic‑similarity distractors, unlabeled video moments, and free‑form temporal localization. Experiments on six vision‑language models reveal varied failure modes, limited cascading between abilities, and differing impacts of audio and subtitles, while a human study shows even experts struggle with concepts from neighboring countries.
arXiv:2605. 16716v5 Announce Type: replace-cross Abstract: Text-to-video (T2V) generation has rapidly progressed in visual fidelity, yet its ability to faithfully represent multiple cultures within a single prompt remains underexplored.
arXiv:2605. 16716v4 Announce Type: replace-cross Abstract: Text-to-video (T2V) generation has rapidly progressed in visual fidelity, yet its ability to faithfully represent multiple cultures within a single prompt remains underexplored.
arXiv:2608.28405v1 Announce Type: new Abstract: Current cultural evaluations for large language models (LLMs) often reduce culture to single-turn factual recall via MCQs, failing to capture a common...