W orld C uisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines (2025)

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.

## introduction food is an essential medium for the exchange of regional cultures, serving to connect diverse peoples and traditions @xcite . analyzing various culinary practices provides valuable insights into the cultural values, historical narratives, and social customs of the communities that produce and consume these foods @xcite . furthermore, food plays a significant role in shaping lan- as a result, similar food concepts can be found across different countries, reflecting a shared human culinary heritage. researchers use food as a proxy to model and analyze cultural dynamics, helping to quantify cultural differences across regions @xcite . many cultures have developed their own versions of "stuffed pasta" or "dumplings", each with unique ingredients and preparation methods, often known by different names @xcite , as illustrated in figure 1 . small details like how the dumpling is shaped can signal the cultural background. conversely, some dishes share the same name but have different meanings; for example, "jelly" in the u.s. refers to a fruit spread, while in the u.k. and parts of asia, it refers to a gelatinous dessert @xcite . this culinary diversity presents a challenge for vision language models (vlms), which must accurately recognize and differentiate food items based on cultural context for applications like food recognition. these models navigate the complexities of names, ingredients, and preparation styles that vary widely across regions. vlms have shown effectiveness in text captioning @xcite and have been adapted to support multiple languages @xcite . however, there is limited research on evaluating the multicultural capabilities of vlms, particularly in terms of multilinguality. the study by @xcite introduce visual question answering (vqa) from a multicultural perspective, but it mainly focuses on knowledge and situational context at a specific moment, which does not fully assess the ability of vlms to reason and differentiate between cultures within a single question. moreover, another study on food vqa is limited to chinese culture and does not explore the broader spectrum of global cultures @xcite ). an earlier investigation into cultural bias in language models also found that cultural knowledge is lacking @xcite . therefore, further research is necessary to address these limitations and enhance our understanding of vlms' multicultural and multilingual capabilities. to facilitate a comprehensive analysis of multilingual and multicultural research, we develop resources for evaluating vlms. ## worldcuisines we propose worldcuisines, an open-source benchmark designed to evaluate the cultural relevance and understanding of vlms. figure 2 displays vqa examples in english, alongside selected parallel translations in japanese and french. ## conclusion we introduce worldcuisines, an open-source, large-scale benchmark designed for multilingual and multicultural, visually grounded language understanding. it comprises over 1 million data points across 30 languages and dialects. our findings reveal that this benchmark remains challenging for vlms, particularly with dishes from specific regions and in low-resource languages. this provides insight into how well models understand regional cuisines. to enhance usability, we offer a dedicated evaluation split with two datasets of varying sizes. our evaluation shows that while vlms perform better with the correct context, they struggle with adversarial contexts intended to mislead them. additionally, we are releasing a comprehensive knowledge base, vqa dataset, code, and leaderboard as open-source resources to support future research. models against our test sets. currently, we include 30 different languages and dialects, establishing one of the largest and most diverse benchmarks for comprehensive multilingual vqa. we aim to extend this benchmark to encompass additional languages in the future, making it more inclusive and representative of a broader range of linguistic diversity. it is important to note that our food entries are currently sourced from english wikipedia. although we aim to include as many diverse dishes as possible, we acknowledge that this approach limits the coverage of some regions. this is due to language affects commonsense and its specific knowledge @xcite , which in turns suggesting insufficiency of sourcing only english wikipedia. nevertheless, our dataset serves as a valuable starting point. in future work, we plan to incorporate entries from non-english wikipedia pages to improve regional representation and cultural diversity. for evaluation purposes, we include accuracy metrics for overall model performance and bertscore for more detailed analysis. however, we recognize that evaluating vqa model performance on multicultural data remains an open challenge. appropriate evaluation metrics are needed to effectively model the diversity of cultural contexts and linguistic variations. addressing this issue will be a key focus of our future research efforts.