# ChinaHeritaQA: A Culturally-Grounded Visual Question Answering Dataset for World Heritage Sites in China Source: [ChinaHeritaQA: A Culturally-Grounded Visual Question Answering Dataset for World Heritage Sites in China]() Collection index: [llms.txt]() Data updated: 2026-06-10T05:01:35.776Z Authors: Yi Zhang, Bolei Ma, Yong Cao, Chengyan Wu, Daniel Hershcovich, Anna-Carolina Haensch Published: 2026-06-08T03:00:45.000Z ## Abstract We introduce ChinaHeritaQA, a multimodal benchmark dataset for evaluating the cultural reasoning abilities of vision-language models (VLMs) on UNESCO World Heritage sites in China. The dataset comprises 2,279 in-the-wild images paired with 14,133 bilingual (Chinese/English) multiple-choice QA pairs spanning seven cognitive dimensions, from basic identity recognition to historical periodization and architectural analysis. Guided by a UNESCO-aligned heritage ontology and verified through rigorous human annotation, the dataset ensures linguistic quality and factual consistency. Evaluations of state-of-the-art VLMs reveal that while top models exceed human performance on average, substantial task-level variation emerges: models excel at visual recognition but struggle with culturally grounded reasoning. Performance also varies by dynasty and region. ChinaHeritaQA reveals that strong visual retrieval does not extend to cultural and historical understanding. We release the dataset to support future research on culturally aware multimodal learning. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()