Visual Question Answering (VQA) is an important task in multimodal AI, and it is often used to test the ability of vision-language models to understand and reason on knowledge present in both visual and textual data. However, most of the current VQA models use datasets that are primarily focused on English and a few major world languages, with images that are typically Western-centric. While recent efforts have tried to increase the number of languages covered on VQA datasets, they still lack diversity in low-resource languages. More importantly, although these datasets often extend their linguistic range via translation or some other approaches, they usually keep images the same, resulting in narrow cultural representation. To address these limitations, we construct CVQA, a new Culturally-diverse multilingual Visual Question Answering benchmark, designed to cover a rich set of languages and cultures, where we engage native speakers and cultural experts in the data collection process. As a result, CVQA includes culturally-driven images and questions from across 30 countries on four continents, covering 31 languages with 13 scripts, providing a total of 10k questions. We then benchmark several Multimodal Large Language Models (MLLMs) on CVQA, and show that the dataset is challenging for the current state-of-the-art models. This benchmark can serve as a probing evaluation suite for assessing the cultural capability and bias of multimodal models and hopefully encourage more research efforts toward increasing cultural awareness and linguistic diversity in this field.
WorldBench: Culturally Grounded Benchmark for Multilingual Agents
University of Edinburgh（爱丁堡大学）；School of Informatics, University of Edinburgh（爱丁堡大学信息学院）；School of Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学人工智能学院）
NativQA: Multilingual Culturally-Aligned Natural Query for LLMs
University of New Brunswick（新不伦瑞克大学）；Qatar Computing Research Institute（卡塔尔计算研究中心）；UPES（UPES大学）；Carnegie Mellon University in Qatar（卡塔尔卡内基梅隆大学）；Qatar University（卡塔尔大学）
LLM Safety Alignment in Low-Resource Languages: A Systematic Literature Review
African Institute for Mathematical Sciences (AIMS)（非洲数学科学研究所）；Bayero University Kano（卡诺贝罗大学）；Brown University（布朗大学）；Centrum Wiskunde & Informatica（数学与计算机科学中心）；University of Pretoria（比勒陀利亚大学）；University of Hamburg（汉堡大学）；Imperial College London（伦敦帝国学院）
MMA-ASIA: A Multilingual and Multimodal Alignment Framework for Culturally-Grounded Evaluation
Singapore University of Technology and Design（新加坡科技设计大学）；Agency for Science, Technology and Research, Singapore（新加坡科技研究局）；Indian Institute of Technology Delhi（印度理工学院德里分校）；Alibaba DAMO Academy（阿里巴巴达摩院）；Microsoft Research Asia（微软亚洲研究院）；Shanghai University of Finance and Economics（上海财经大学）；Inner Mongolia University（内蒙古大学）；Kyoto University（京都大学）；Jiangxi Normal University（江西师范大学）；Korea University（韩国大学）；Nanyang Technological University（南洋理工大学）
Whose Morality Do They Speak? Unraveling Cultural Bias in Multilingual Language Models
Research Center Trustworthy Data Science and Security of the University Alliance Ruhr（鲁尔大学联盟可信数据科学与安全研究中心）；Faculty of Informatics, Technical University Dortmund（多特蒙德工业大学信息学院）
Why Vision Fails as a Universal Bridge: Rectifying Modality Asynchrony in Multilingual MLLMs
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Loop Area Institute（深圳河套学院）；National Health Data Institute (Shenzhen)（国家健康数据研究院（深圳））
Many Dialects, Many Languages, One Cultural Lens: Evaluating Multilingual VLMs for Bengali Culture Understanding Across Historically Linked Languages and Regional Dialects
United International University（国际联合大学）；BRAC University（布拉塔克大学）；University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）
MELLA: Bridging Linguistic Capability and Cultural Groundedness for Low-Resource Language MLLMs
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；East China Normal University（东华大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Institute of High Performance Computing, A*STAR（高性能计算研究所，A*STAR）
Alif: Advancing Urdu Large Language Models via Multilingual Synthetic Data Distillation
University of British Columbia（不列颠哥伦比亚大学）；Texas Tech University（德克萨斯技术大学）；Institute for the Future of Education, Tecnológico de Monterrey（教育未来研究所，墨西哥蒙特雷技术学院）
TowerVision: Understanding and Improving Multilinguality in Vision-Language Models
Instituto Superior Técnico, Universidade de Lisboa（里斯本大学技术高级学院）；Instituto de Telecomunicações（电信研究所）；Carnegie Mellon University（卡内基梅隆大学）；Sword Health（Sword健康）；TransPerfect；MICS, CentraleSupélec, Université Paris-Saclay（MICS，中央圣艾尔布里大学，巴黎萨克雷大学）；ELLIS Unit Lisbon（里斯本ELLIS单位）
Same Values, Different Languages? From Multilingual Probing to Steering LLMs Toward Chinese Social Values
School of Information Science and Technology, Beijing Foreign Studies University（北京外国语大学信息科学与技术学院）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Beihang University（北京航空航天大学）
When Meaning Travels: A Granular Lens on Hybrid-MoE's Role in Idiomatic Understanding for Language Models
Department of Computer Science and Engineering, Indian Institute of Technology Patna, India（印度理工学院帕纳瓦分校计算机科学与工程系）；School of Information Technology, King Mongkut’s Institute of Technology Ladkrabang, Thailand（泰国拉差班国王理工大学信息科技学院）
