WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines (2025.naacl-long)
Copied to clipboard
Genta Indra Winata, Frederikus Hudi, Patrick Amadeus Irawan, David Anugraha, Rifki Afina Putri, Wang Yutong, Adam Nohejl, Ubaidillah Ariq Prathama, Nedjma Ousidhoum, Afifa Amriani, Anar Rzayev, Anirban Das, Ashmari Pramodya, Aulia Adila, Bryan Wilie, Candy Olivia Mawalim, Cheng Ching Lam, Daud Abolade, Emmanuele Chersoni, Enrico Santus, Fariz Ikhwantri, Garry Kuwanto, Hanyang Zhao, Haryo Akbarianto Wibowo, Holy Lovenia, Jan Christian Blaise Cruz, Jan Wira Gotama Putra, Junho Myung, Lucky Susanto, Maria Angelica Riera Machin, Marina Zhukova, Michael Anugraha, Muhammad Farid Adilazuarda, Natasha Christabelle Santosa, Peerat Limkonchotiwat, Raj Dabre, Rio Alexander Audino, Samuel Cahyawijaya, Shi-Xiong Zhang, Stephanie Yulia Salim, Yi Zhou, Yinxuan Gui, David Ifeoluwa Adelani, En-Shiun Annie Lee, Shogo Okada, Ayu Purwarianti, Alham Fikri Aji, Taro Watanabe, Derry Tanti Wijaya, Alice Oh, Chong-Wah Ngo
| Challenge: | Vision Language Models struggle with cultural-specific knowledge, especially in languages other than English and in underrepresented cultural contexts. |
| Approach: | They propose a visual question answering (VQA) dataset with text-image pairs across 30 languages and dialects and a training dataset. |
| Outcome: | The proposed model performs better with correct location context, but struggles with adversarial contexts and predicting specific regional cuisines and languages. |
Similar Papers
Benchmarking Vision Language Models for Cultural Understanding (2024.emnlp-main)
Copied to clipboard
Shravan Nayak, Kanishk Jain, Rabiul Awal, Siva Reddy, Sjoerd Steenkiste, Lisa Hendricks, Karolina Stanczak, Aishwarya Agrawal
| Challenge: | Recent multimodal vision-language models have shown impressive performance in tasks such as image-to-text generation, visual question answering, and image captioning. |
| Approach: | They propose a visual question-answering benchmark to assess VLMs' cultural understanding of various facets of culture from 11 countries across 5 continents. |
| Outcome: | The visual question-answering benchmark aims to assess VLMs' cultural understanding across regions. |
MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering (2025.findings-acl)
Copied to clipboard
Jingqun Tang, Qi Liu, Yongjie Ye, Jinghui Lu, Shu Wei, An-Lan Wang, Chunhui Lin, Hao Feng, Zhen Zhao, Yanjie Wang, Yuliang Liu, Hao Liu, Xiang Bai, Can Huang
| Challenge: | Text-Centric Visual Question Answering (TEC-VQA) is a text-centric visual task understanding tool. |
| Approach: | They introduce a benchmark that features human expert annotations across 9 languages . they prioritize the text in question-answer pairs while disregarding visual text in images . |
| Outcome: | The proposed benchmarks prioritize the text in question-answer pairs while disregarding visual text in images. |
POLYCHARTQA: Benchmarking Large Vision-Language Models with Multilingual Chart Question Answering (2026.acl-long)
Copied to clipboard
| Challenge: | Existing chart understanding benchmarks are overwhelmingly English-centric, limiting their accessibility and relevance to global audiences. |
| Approach: | They propose a multilingual chart question answering benchmark that enables efficient multilingual generation via data translation and code reuse. |
| Outcome: | The proposed benchmark systematically evaluates multilingual chart understanding on state-of-the-art LVLMs and shows a significant performance gap between English and other languages. |
MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering (2021.tacl-1)
Copied to clipboard
| Challenge: | Existing multilingual QA datasets lack linguistic diversity and comparable evaluation between languages. |
| Approach: | They propose a multilingual question-answer evaluation set with 10k English queries and human translations of them into 25 additional languages and dialects. |
| Outcome: | The proposed model is based on a multilingual knowledge questions and answers evaluation set with 26 languages. |
TVQACML: Benchmarking Text-Centric Visual Question Answering in Multilingual Chinese Minority Languages (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing TEC-VQA benchmarks focus on high-resource languages like English and Chinese . existing benchmarks have a "visual-textual misalignment" problem resulting in unreliable evaluation results . |
| Approach: | They propose a benchmark that expands multilingual QA pairs in non-text-centric datasets through translation to eight languages, including Standard Chinese, Korean, and six minority languages. |
| Outcome: | The proposed benchmarks are contamination-free and more challenging . they include eight languages including Chinese, Korean, and six minority languages . |
BLEnD-Vis: Benchmarking Multimodal Cultural Understanding in Vision Language Models (2026.eacl-long)
Copied to clipboard
Bryan Chen Zhengyu Tan, Weihua Zheng, Zhengyuan Liu, Nancy F. Chen, Hwaran Lee, Kenny Tsu Wei Choo, Roy Ka-Wei Lee
| Challenge: | Existing evaluations assess static recall or isolated visual grounding, leaving unanswered whether VLMs possess robust and transferable cultural understanding. |
| Approach: | They propose a multimodal, multicultural benchmark to evaluate the robustness of everyday cultural knowledge in vision-language models across linguistic rephrasings and visual modalities. |
| Outcome: | ‘BLEnD-Vis‘ constructs 313 culturally grounded question templates spanning 16 regions and generates three aligned multiple-choice formats. |
Evaluating Visual and Cultural Interpretation: The K-Viscuit Benchmark with Human-VLM Collaboration (2025.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to creating inclusive vision-language models rely on human annotators, making it labor-intensive and creating cognitive burdens. |
| Approach: | They propose a semi-automated framework for constructing cultural VLM benchmarks . they use an annotated sample of Korean culture to generate questions . |
| Outcome: | The proposed framework is based on a Korean culture dataset and shows that open-source models lag behind proprietary ones in understanding Korean culture. |
CROPE: Evaluating In-Context Adaptation of Vision and Language Models to Culture-Specific Concepts (2025.naacl-long)
Copied to clipboard
| Challenge: | Recent Vision and Language models have shown impressive performance across benchmarks . however, frontier models lack cultural awareness and can affect global cultural diversity . |
| Approach: | They propose a visual question answering benchmark to probe the knowledge of culture-specific concepts and evaluate the capacity for cultural adaptation through contextual information. |
| Outcome: | The proposed model shows large performance disparities between culture-specific and common concepts in the parametric setting. |
M5 – A Diverse Benchmark to Assess the Performance of Large Multimodal Models Across Multilingual and Multicultural Vision-Language Tasks (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Recent advances in Large Language Models and their multimodal counterparts have shown significant performance disparities across different languages and cultural contexts. |
| Approach: | They propose to evaluate LLMs on diverse vision-language tasks within a multilingual and multicultural context using M5 benchmark. |
| Outcome: | The proposed benchmarks highlight task-agnostic performance disparities between languages and cultural contexts. |
Seeing Culture: A Benchmark for Visual Reasoning and Grounding (2025.emnlp-main)
Copied to clipboard
Burak Satar, Zhixin Ma, Patrick Amadeus Irawan, Wilfried Ariel Mulyawan, Jing Jiang, Ee-Peng Lim, Chong-Wah Ngo
| Challenge: | Multimodal vision-language models (VLMs) have made significant progress in cultural understanding tasks . but these datasets often fall short of providing cultural reasoning while underrepresenting many cultures. |
| Approach: | They propose a Seeing Culture Benchmark that requires VLMs to reason on culturally rich images in two stages. |
| Outcome: | The proposed approach requires VLMs to reason on culturally rich images in two stages . the Seeing Culture Benchmark identifies cultural reasoning shortcomings in multimodal models . |