WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines
On the map: Multimodal LLMs / vision-language models → VLM benchmarks and visual reasoning
Abstract (arXiv)
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.
Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 21 of its 55 references that are in the map. First posted on arXiv 16 Oct 2024.
Builds on
21 of 55 references.
Closest ancestors · 3
Same direction, newest first
- 2024Benchmarking Vision Language Models for Cultural Understanding15 Jul
- 2024CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark10 Jun
- 2023mBLIP: Efficient Bootstrapping of Multilingual Vision-LLMs13 Jul
Other references · 18
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- 2024FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food Culturecited by 25
- 2024MINERS: Multilingual Language Models as Semantic Retrieverscited by 11
- 2024X-LLaVA: Optimizing Bilingual Large Vision-Language Alignmentcited by 10
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Citing papers per year: 2024: 3; 2025: 25; 2026: 11.
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