arXiv AI
Jun 8
CULTURESCORE: Evaluating Cultural Faithfulness in Video Generation Models
arXiv:2606. 07311v1 Announce Type: cross Abstract: As video generation models like Veo 3.
VideoNorms: Benchmarking Cultural Awareness of Video Language Models
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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arXiv AI
Jul 21
When Cultures Move: Measuring and Improving Multicultural Text-to-Video Generation
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 AI
Jun 6
MAVEN A Multi-Agent Framework for Multicultural Text-to-Video Generation
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 AI
5d ago
NARU: A Benchmark for NARrative Evolution and Cultural Nuance Understanding in Japanese Extreme Long Video
arXiv:2608. 13210v1 Announce Type: cross Abstract: Long-form video understanding encompasses tasks that go beyond retrieving isolated events, including tracking an evolving narrative and interpreting social meaning that may remain implicit.
Hugging Face Trending Papers
Jun 8
ChinaHeritaQA: A Culturally-Grounded Visual Question Answering Dataset for World Heritage Sites in China
We introduce ChinaHeritaQA, a multimodal benchmark dataset for evaluating the cultural reasoning abilities of vision-language models (VLMs) on UNESCO World Heritage sites in China. The dataset comprises 2,279 in-the-wild images paired with 14,133 bilingual (Chinese/English) multiple-choice QA pairs spanning seven cognitive dimensions, from basic identity recognition to historical periodization and architectural analysis.
arXiv AI
Jul 8
CCBENCH: Assessing LLM Cultural Competence via Implicitly Signaled Norms using Health Queries
arXiv:2607. 05405v1 Announce Type: cross Abstract: To interact with users fairly and without stereotyping, AI models must display cultural competency, i.
arXiv AI
Jun 9
Multilingual Training and Evaluation Resources for Vision-Language Models
arXiv:2604. 18347v2 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) achieved rapid progress in the recent years.
arXiv Machine Learning
Jun 2
Quantifying the Salience of Geo-Cultural Values for Pluralistic Safety Alignment
arXiv:2606. 00369v1 Announce Type: cross Abstract: Safe global deployment of AI models requires alignment with human values that vary across cultures.
arXiv AI
Jul 20
Debiasing Text-to-Image Evaluation via Implicit Cultural Alignment Reward Modeling
arXiv:2607. 15740v1 Announce Type: cross Abstract: As Text-to-Image (T2I) systems rapidly advance, evaluating the cultural authenticity of synthesized content has become increasingly important for fair and trustworthy generative AI.
arXiv AI
Jul 23
On the Systematic Challenges of Culturally Loaded Machine Translation: Dream of the Red Chamber as the Cultural Lens
arXiv:2607. 20241v1 Announce Type: cross Abstract: Culturally loaded translation poses unique challenges for machine translation (MT), as meanings are deeply embedded in socio-cultural contexts beyond surface linguistic forms.
arXiv AI
6d ago
How China-Origin Vision-Language Models Move from Refusal to Reframing in State Alignment
arXiv:2608. 11816v1 Announce Type: cross Abstract: State-aligned distortion has been documented in China-origin text-based large language models (LLMs), but whether, and in what form, it arises in multimodal systems has not been systematically examined.
arXiv AI
Jul 24
MELLA: Bridging Linguistic Capability and Cultural Groundedness for Low-Resource Language MLLMs
arXiv:2508. 05502v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) perform strongly in high-resource languages, yet often produce fluent but culturally "thin" descriptions in low-resource settings.