WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines (2410.12705v5)

Abstract: Vision LLMs (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.

• The paper introduces a massive-scale benchmark with over 1 million text-image pairs across 30 languages to test multicultural visual question answering on global cuisines.
• The paper details a rigorous methodology involving multilingual annotation and cultural categorization to capture nuanced regional culinary data.
• The paper reveals that while correct cultural context enhances VLM accuracy, adversarial scenarios expose significant gaps in multilingual reasoning.

Insights into $black$: A Benchmark for Multilingual and Multicultural Visual Question Answering

This essay examines the paper titled "$black$: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines," which introduces a comprehensive and extensive dataset designed to assess Vision LLMs (VLMs) in culturally diverse contexts. The benchmark focuses primarily on the domain of global cuisines, providing valuable insights into the performance of VLMs when exposed to multilingual and multicultural visual question-answering (VQA) challenges.

Overview and Methodology

The paper identifies a critical gap in existing vision-language benchmarks, which predominantly cater to English and overlook the complexities inherent in culturally specific knowledge across different languages. To address this, the authors develop a large-scale benchmark dubbed $black$, comprising over 1 million text-image pairs covering 30 languages and dialects across 9 language families. This benchmark represents the largest multicultural VQA dataset available and includes nuanced tasks such as identifying dish names and their cultural origins.

The dataset construction involves meticulous annotation work, including the curation of visual representations, categorical classification of dishes, and the translation of questions into multiple languages by native speakers. This allows the benchmark to assess VLMs on complex tasks involving regional cuisines under varying context scenarios, from contextualized to adversarial challenges. The adversarial contexts are particularly significant as they test the robustness of models against misleading information.

Numerical Results and Claims

The results derived from evaluating various open-source and proprietary VLMs illuminate both the strengths and limitations of these models in culturally charged settings. Notably, proprietary models like GPT-4o displayed superior performance, with accuracy rates significantly higher than those of the open-source counterparts. The incorporation of context (e.g., correct cultural or regional cues) generally improved model predictions, whereas adversarial contexts tended to confuse and mislead the models, pointing to a critical area for improvement in VLM robustness and reasoning.

Furthermore, the performance of VLMs varied significantly across language families, with better outcomes in languages with rich resources compared to low-resource languages. The benchmark revealed that non-Latin script languages generally posed more of a challenge in open-ended questions, suggesting a need for enhanced linguistic diversity in training data for VLMs.

Theoretical and Practical Implications

The introduction of $black$ offers significant theoretical and practical implications. Theoretically, it advances the understanding of cross-cultural reasoning within artificial intelligence, highlighting the necessity for future multilingual models to incorporate deeper cultural and contextual nuances. Practically, the benchmark establishes a foundation for developing AI applications in multicultural settings, from cooking-assistant systems that can cater to global cuisines to enhanced recommendation systems that respect diverse cultural food practices.

Future Directions

Given the current limitations identified in VLM performance, this benchmark sets the stage for future research aimed at improving cultural sensitivity and multilingual reasoning capabilities in AI systems. As the demand for AI systems that can operate in culturally diverse environments grows, the continual development and expansion of resources like $black$ become increasingly vital. Importantly, the benchmark encourages the AI research community to focus on constructing models that are not only larger and more powerful but also more inclusive and contextually aware.

In conclusion, this paper's benchmark represents a significant step toward addressing the complexity of multicultural understanding in AI systems. By providing a large-scale testbed for VLMs, $black$ offers critical insights and sets a high standard for future advancements in culturally aware AI.