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）
Large language models (LLMs) are now used worldwide, yet their multimodal understanding and reasoning often degrade outside Western, high-resource settings. We propose MMA-ASIA, a comprehensive framework to evaluate LLMs' cultural awareness with a focus on Asian contexts. MMA-ASIA centers on a human-curated, multilingual, and multimodally aligned multiple-choice benchmark covering 8 Asian countries and 10 languages, comprising 27,000 questions; over 79 percent require multi-step reasoning grounded in cultural context, moving beyond simple memorization. To our knowledge, this is the first dataset aligned at the input level across three modalities: text, image (visual question answering), and speech. This enables direct tests of cross-modal transfer. Building on this benchmark, we propose a five-dimensional evaluation protocol that measures: (i) cultural-awareness disparities across countries, (ii) cross-lingual consistency, (iii) cross-modal consistency, (iv) cultural knowledge generalization, and (v) grounding validity. To ensure rigorous assessment, a Cultural Awareness Grounding Validation Module detects "shortcut learning" by checking whether the requisite cultural knowledge supports correct answers. Finally, through comparative model analysis, attention tracing, and an innovative Vision-ablated Prefix Replay (VPR) method, we probe why models diverge across languages and modalities, offering actionable insights for building culturally reliable multimodal LLMs.
CultureConverse: A Multilingual Multi-turn Simulation Harness for Culturally Grounded Assistance in East and Southeast Asia
Singapore University of Technology and Design (SUTD)（新加坡科技设计大学）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）；École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；Microsoft Research Asia (MSRA)（微软亚洲研究院）；Sungkyunkwan University (SKKU)（成均馆大学）；Universiti Brunei Darussalam (UBD)（文莱大学）；China University of Petroleum (East China)（中国石油大学（华东））；Nanyang Technological University (NTU)（南洋理工大学）；Kyoto University（京都大学）；Konkuk University（建国大学）；Upstage AI
Camellia: Benchmarking Cultural Biases in LLMs for Asian Languages
Georgia Institute of Technology（佐治亚理工学院）；Samsung R&D Institute Philippines（三星菲律宾研发院）；Sungkyunkwan University（成均馆大学）；Tohoku University（东北大学）；National University of Singapore（新加坡国立大学）；University of Copenhagen（哥本哈根大学）；University of Michigan（密歇根大学）；Indian Institute of Technology Delhi（印度理工学院德里）；Institute of Science Tokyo（东京科学研究院）
LaoBench: A Large-Scale Multidimensional Lao Benchmark for Large Language Models
China-ASEAN Information Harbor Co., Ltd.（中国-东盟信息港有限公司）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；School of Software & Microelectronics, Peking University（北京大学软件与微电子学院）
SAGE: Sustainable Agent-Guided Expert-tuning for Culturally Attuned Translation in Low-Resource Southeast Asia
Xi'an Jiaotong-Liverpool University（西安交通大学利物浦大学）；University of Liverpool（利物浦大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Counterfactual Cultural Cues Reduce Medical QA Accuracy in LLMs: Identifier vs Context Effects
Institute of Health Policy, Management, and Evaluation (IHPME)（健康政策、管理与评估研究所）；Dalla Lana School of Public Health（达拉兰公共卫生学院）；University of Toronto（多伦多大学）；Faculty of Information（信息学院）；Schwartz Reisman Institute（施瓦茨-雷曼研究所）
Transcending Language Boundaries: Harnessing LLMs for Low-Resource Language Translation
School of Computing, The University of Georgia（佐治亚大学计算学院）；Second Language Acquisition and Teaching, University of Arizona（亚利桑那大学第二语言习得与教学系）；University of Alberta（阿尔伯塔大学）；Department of History, The University of Georgia（佐治亚大学历史系）；National GENIUS Center（国家GENIUS中心）；AI4STEM Education Center, University of Georgia（佐治亚大学AI4STEM教育中心）；Department of Mathematics, Science, and Social Studies Education, University of Georgia（佐治亚大学数学、科学与社会研究教育系）
CultureVLM: Characterizing and Improving Cultural Understanding of Vision-Language Models for over 100 Countries
University of Macau（澳门大学）；Georgia Institute of Technology（佐治亚理工学院）；Microsoft Research（微软研究院）；Squirrel AI（松鼠AI）；Lehigh University（理海大学）；William & Mary（威廉玛丽学院）
MemeCULT-1K: Benchmarking South Asian Cultural Context and Humor Understanding of Multimodal Models
Islamic University of Technology（伊斯兰科技大学）；South East University（东南大学）；Vector Institute（矢量研究所）；University of British Columbia（不列颠哥伦比亚大学）；York University（约克大学）
Multilingual Dyadic Interaction Corpus NoXi+J: Toward Understanding Asian-European Non-verbal Cultural Characteristics and their Influences on Engagement
University of Augsburg（奥格斯堡大学）；Japan Advanced Institute of Science and Technology（日本先进科学技术学院）
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）
