CVQA: Culturally-diverse Multilingual Visual Question Answering
  Benchmark
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 28
countries on four continents, covering 26 languages with 11 scripts, providing
a total of 9k 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.
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David Romero
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Chenyang Lyu
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Chenyang Lyu
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Haryo Akbarianto Wibowo
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Aishik Mandal
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Atnafu Lambebo Tonja
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2024-09-03T07:50:14.586Z
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Atnafu Lambebo Tonja
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Bontu Fufa Balcha
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Chenxi Whitehouse
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Christian Salamea
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2024-12-06T15:58:21.285Z
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David Adelani
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Henok Biadglign Ademtew
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Henok Ademtew
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Hernán Maina
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Holy Lovenia
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2024-06-18T07:20:10.842Z
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Holy Lovenia
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Israel Abebe Azime
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2025-05-14T07:38:05.293Z
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Israel Abebe Azime
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Jan Christian Blaise Cruz
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Jan Christian Blaise Cruz
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Jesus-German Ortiz-Barajas
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Kumaranage Ravindu Yasas Nagasinghe
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Luis Fernando D'Haro
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Marcelo Viridiano
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Marcos Estecha-Garitagoitia
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Maria Camila Buitrago Cabrera
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Mario Rodríguez-Cantelar
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Mihail Mihaylov
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Mohamed Fazli Mohamed Imam
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Muhammad Farid Adilazuarda
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Ruochen Zhang
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Samuel Cahyawijaya
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Santiago Góngora
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Santiago Góngora
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Sukannya Purkayastha
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Tatsuki Kuribayashi
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Tiago Timponi Torrent
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CVQA: Culturally-diverse Multilingual Visual Question Answering
  Benchmark
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 28
countries on four continents, covering 26 languages with 11 scripts, providing
a total of 9k 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.
Culturally-diverse multilingual Visual Question Answering benchmark (CVQA) covers 28 countries, 26 languages, and 11 scripts, testing the cultural capability and linguistic diversity of multimodal models.
Visual Question Answering (VQA) multimodal AI vision-language models CVQA Culturally-diverse multilingual Visual Question Answering benchmark Multimodal Large Language Models (MLLMs) cultural capability linguistic diversity