Cultural awareness capabilities have emerged as a critical capability for Multimodal Large Language Models (MLLMs). However, current benchmarks lack progressed difficulty in their task design and are deficient in cross-lingual tasks. Moreover, current benchmarks often use real-world images. Each real-world image typically contains one culture, making these benchmarks relatively easy for MLLMs. Based on this, we propose C$^3$B (Comics Cross-Cultural Benchmark), a novel multicultural, multitask and multilingual cultural awareness capabilities benchmark. C$^3$B comprises over 2000 images and over 18000 QA pairs, constructed on three tasks with progressed difficulties, from basic visual recognition to higher-level cultural conflict understanding, and finally to cultural content generation. We conducted evaluations on 11 open-source MLLMs, revealing a significant performance gap between MLLMs and human performance. The gap demonstrates that C$^3$B poses substantial challenges for current MLLMs, encouraging future research to advance the cultural awareness capabilities of MLLMs.
Culture-TRIP: Culturally-Aware Text-to-Image Generation with Iterative Prompt Refinement
Department of Computer Science and Engineering, Dongguk University（计算机科学与工程系，东国大学）；Department of Computer Science and Artificial Intelligence, Dongguk University（计算机科学与人工智能系，东国大学）
Creating a Lens of Chinese Culture: A Multimodal Dataset for Chinese Pun Rebus Art Understanding
University of Southern California（南加州大学）；Shanghai International Studies University（上海外国语大学）；The Butler Museum（巴特勒博物馆）；Jingdezhen Imperial Kiln Institute（景德镇御窑研究院）；The Ohio State University（俄亥俄州立大学）
本文构建深植中国传统文化的多模态 Pun Rebus Art Dataset，通过元素识别、象征匹配和信息解释任务评估 VLM，揭示其文化理解短板并推动更具包容性的模型发展。
From Words to Worlds: Benchmarking Cross-Cultural Cultural Understanding in Machine Translation
Xinjiang Technical Institute of Physics \& Chemistry, Chinese Academy of Sciences Urumqi China；Xinjiang Technical Institute of Physics \& Chemistry, Chinese Academy of Sciences
CrossCult-KIBench: A Benchmark for Cross-Cultural Knowledge Insertion in MLLMs
Hefei University of Technology（合肥工业大学）；Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China（民族语言智能分析与安全治理国家重点实验室，中央民族大学）；School of Information Engineering, Minzu University of China（中央民族大学信息工程学院）
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）
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）
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）
On the Systematic Challenges of Culturally Loaded Machine Translation: Dream of the Red Chamber as the Cultural Lens
School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学与工程系）；School of Foreign Languages, East China University of Science and Technology（华东理工大学外国语学院）；Mejiro University（目白大学）
Evaluating and Improving Cultural Awareness of Reward Models for LLM Alignment
Institute of Computing and Intelligence, Harbin Institute of Technology, Shenzhen, China（计算与智能研究所，哈尔滨工业大学，深圳，中国）；Peng Cheng Laboratory, Shenzhen, China（鹏城实验室，深圳，中国）
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）
Seeing Symbols, Missing Cultures: Probing Vision-Language Models' Reasoning on Fire Imagery and Cultural Meaning
Duncan of Jordanstone College of Art & Design (DJCAD), University of Dundee（邓迪大学邓迪艺术与设计学院（DJCAD））；Guangzhou Institute of Science and Technology (GZIST)（广州科学技术研究院）；Faculty of Arts, Xiamen University（厦门大学艺术学院）；University of Birmingham（伯明翰大学）
Towards culturally-appropriate conversational AI for health in the majority world: An exploratory study with citizens and professionals in Latin America
Imperial College London（帝国理工学院）；University of Cambridge（剑桥大学）；La Victoria Lab（拉维多利亚实验室）；Universidad Nacional de Córdoba（科尔多瓦国立大学）
CulturePark: Boosting Cross-cultural Understanding in Large Language Models
Institute of Software, CAS（中国科学院软件研究所）；Idiap Research Institute（Idiap研究所）；Westlake University（西湖大学）；Squirrel AI（松鼠AI）；Microsoft Research（微软研究院）；William & Mary（威廉玛丽学院）
XCR-Bench: Benchmarking Cross-Cultural Reasoning in LLMs via Culture-Specific Items and Hall's Triad
Department of Computer Science, National Centre for Text Mining, The University of Manchester（计算机科学系，国家文本挖掘中心，曼彻斯特大学）；ELLIS Manchester（曼彻斯特ELLIS）；School of Computing, Queen’s University, Ontario, Canada（计算学院，加拿大皇后大学）；Computer Science, University of Illinois Chicago（计算机科学，伊利诺伊大学芝加哥分校）；ELLIS Institute Finland（芬兰ELLIS研究所）；University of Turku（图尔库大学）；Department of Computer Science and Artificial Intelligence, Umm Al-Qura University, Makkah, Saudi Arabia（计算机科学与人工智能系，乌姆·阿勒·卡拉大学，麦加，沙特阿拉伯）
Where Culture Fades: Revealing the Cultural Gap in Text-to-Image Generation
The University of Sydney（悉尼大学）；Nanjing University of Science and Technology（南京理工大学）；Central South University（中南大学）；Nanjing University（南京大学）；National University of Singapore（新加坡国立大学）
