myFoodQA: A Multimodal Dataset for Evaluating Cultural and Visual Reasoning in Myanmar Gastronomy - Shin Thant Phyo; Pyae Linn; Thet Hmue Khin; Lynn Myat Bhone; Eaint Kay Khaing Kyaw; Ye Kyaw Thu (Zenodo (CERN European Organization for Nuclear Research), 2025)
This paper presents myFoodQA (Myanmar Food Question Answering), the first multimodal benchmark focused on Myanmar's rich gastronomic culture. We constructed a novel dataset containing 2,485 question-answer pairs covering 20 distinct dishes, with all data sourced from personal photography and web-crawling and subsequently validated by native Burmese speakers for authenticity. The benchmark is designed to test single-image, multi-image, and text-only reasoning, evaluating a model's understanding of ingredients, cultural context, preparation methods, and comparative logic. Our zero-shot evaluation of leading vision-language models (VLMs) reveals a significant performance gap: while models perform well on text-based tasks, they show a significant deficit in image-based reasoning, which requires specific visual understanding and deep cultural knowledge. These findings expose the limitations of current models in the Myanmar gastronomic domain. We establish myFoodQA as a foundational resource for advancing culturally-aware multimodal AI, particularly in low-resource settings.