@inproceedings{winata-etal-2025-worldcuisines,
title = "{W}orld{C}uisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines",
author = "Winata, Genta Indra and
Hudi, Frederikus and
Irawan, Patrick Amadeus and
Anugraha, David and
Putri, Rifki Afina and
Yutong, Wang and
Nohejl, Adam and
Prathama, Ubaidillah Ariq and
Ousidhoum, Nedjma and
Amriani, Afifa and
Rzayev, Anar and
Das, Anirban and
Pramodya, Ashmari and
Adila, Aulia and
Wilie, Bryan and
Mawalim, Candy Olivia and
Lam, Cheng Ching and
Abolade, Daud and
Chersoni, Emmanuele and
Santus, Enrico and
Ikhwantri, Fariz and
Kuwanto, Garry and
Zhao, Hanyang and
Wibowo, Haryo Akbarianto and
Lovenia, Holy and
Cruz, Jan Christian Blaise and
Putra, Jan Wira Gotama and
Myung, Junho and
Susanto, Lucky and
Machin, Maria Angelica Riera and
Zhukova, Marina and
Anugraha, Michael and
Adilazuarda, Muhammad Farid and
Santosa, Natasha Christabelle and
Limkonchotiwat, Peerat and
Dabre, Raj and
Audino, Rio Alexander and
Cahyawijaya, Samuel and
Zhang, Shi-Xiong and
Salim, Stephanie Yulia and
Zhou, Yi and
Gui, Yinxuan and
Adelani, David Ifeoluwa and
Lee, En-Shiun Annie and
Okada, Shogo and
Purwarianti, Ayu and
Aji, Alham Fikri and
Watanabe, Taro and
Wijaya, Derry Tanti and
Oh, Alice and
Ngo, Chong-Wah",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.167/",
pages = "3242--3264",
ISBN = "979-8-89176-189-6",
abstract = "Vision Language Models (VLMs) often struggle with culture-specific knowledge, particularly in languages other than English and in underrepresented cultural contexts. To evaluate their understanding of such knowledge, we introduce WorldCuisines, a massive-scale benchmark for multilingual and multicultural, visually grounded language understanding. This benchmark includes a visual question answering (VQA) dataset with text-image pairs across 30 languages and dialects, spanning 9 language families and featuring over 1 million data points, making it the largest multicultural VQA benchmark to date. It includes tasks for identifying dish names and their origins. We provide evaluation datasets in two sizes (12k and 60k instances) alongside a training dataset (1 million instances). Our findings show that while VLMs perform better with correct location context, they struggle with adversarial contexts and predicting specific regional cuisines and languages. To support future research, we release a knowledge base with annotated food entries and images along with the VQA data."
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<abstract>Vision Language Models (VLMs) often struggle with culture-specific knowledge, particularly in languages other than English and in underrepresented cultural contexts. To evaluate their understanding of such knowledge, we introduce WorldCuisines, a massive-scale benchmark for multilingual and multicultural, visually grounded language understanding. This benchmark includes a visual question answering (VQA) dataset with text-image pairs across 30 languages and dialects, spanning 9 language families and featuring over 1 million data points, making it the largest multicultural VQA benchmark to date. It includes tasks for identifying dish names and their origins. We provide evaluation datasets in two sizes (12k and 60k instances) alongside a training dataset (1 million instances). Our findings show that while VLMs perform better with correct location context, they struggle with adversarial contexts and predicting specific regional cuisines and languages. To support future research, we release a knowledge base with annotated food entries and images along with the VQA data.</abstract>
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%0 Conference Proceedings
%T WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines
%A Winata, Genta Indra
%A Hudi, Frederikus
%A Irawan, Patrick Amadeus
%A Anugraha, David
%A Putri, Rifki Afina
%A Yutong, Wang
%A Nohejl, Adam
%A Prathama, Ubaidillah Ariq
%A Ousidhoum, Nedjma
%A Amriani, Afifa
%A Rzayev, Anar
%A Das, Anirban
%A Pramodya, Ashmari
%A Adila, Aulia
%A Wilie, Bryan
%A Mawalim, Candy Olivia
%A Lam, Cheng Ching
%A Abolade, Daud
%A Chersoni, Emmanuele
%A Santus, Enrico
%A Ikhwantri, Fariz
%A Kuwanto, Garry
%A Zhao, Hanyang
%A Wibowo, Haryo Akbarianto
%A Lovenia, Holy
%A Cruz, Jan Christian Blaise
%A Putra, Jan Wira Gotama
%A Myung, Junho
%A Susanto, Lucky
%A Machin, Maria Angelica Riera
%A Zhukova, Marina
%A Anugraha, Michael
%A Adilazuarda, Muhammad Farid
%A Santosa, Natasha Christabelle
%A Limkonchotiwat, Peerat
%A Dabre, Raj
%A Audino, Rio Alexander
%A Cahyawijaya, Samuel
%A Zhang, Shi-Xiong
%A Salim, Stephanie Yulia
%A Zhou, Yi
%A Gui, Yinxuan
%A Adelani, David Ifeoluwa
%A Lee, En-Shiun Annie
%A Okada, Shogo
%A Purwarianti, Ayu
%A Aji, Alham Fikri
%A Watanabe, Taro
%A Wijaya, Derry Tanti
%A Oh, Alice
%A Ngo, Chong-Wah
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F winata-etal-2025-worldcuisines
%X Vision Language Models (VLMs) often struggle with culture-specific knowledge, particularly in languages other than English and in underrepresented cultural contexts. To evaluate their understanding of such knowledge, we introduce WorldCuisines, a massive-scale benchmark for multilingual and multicultural, visually grounded language understanding. This benchmark includes a visual question answering (VQA) dataset with text-image pairs across 30 languages and dialects, spanning 9 language families and featuring over 1 million data points, making it the largest multicultural VQA benchmark to date. It includes tasks for identifying dish names and their origins. We provide evaluation datasets in two sizes (12k and 60k instances) alongside a training dataset (1 million instances). Our findings show that while VLMs perform better with correct location context, they struggle with adversarial contexts and predicting specific regional cuisines and languages. To support future research, we release a knowledge base with annotated food entries and images along with the VQA data.
%U https://aclanthology.org/2025.naacl-long.167/
%P 3242-3264
Markdown (Informal)
[WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines](https://aclanthology.org/2025.naacl-long.167/) (Winata et al., NAACL 2025)
ACL
- 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, and Chong-Wah Ngo. 2025. WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 3242–3264, Albuquerque, New Mexico. Association for Computational Linguistics.