title: WorldCuisines: A Massive-Scale Benchmark for Multilingual and
    Multicultural Visual Question Answering on Global Cuisines
summary: 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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paper_title: WorldCuisines: A Massive-Scale Benchmark for Multilingual and
    Multicultural Visual Question Answering on Global Cuisines
paper_summary: 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.
paper_ai_summary: WorldCuisines, a large-scale multilingual and multicultural visual question answering benchmark, evaluates Vision Language Models' understanding of cuisine-specific knowledge across various languages and cultural contexts.
paper_ai_keywords: visually grounded language understanding visual question answering language families knowledge base annotated food entries