A multimodal benchmark dataset for evaluating large language models on traditional Chinese opera understanding

Benchmarking is essential for evaluating the capabilities of large language models (LLMs). However, existing multimodal benchmarks lack dedicated resources for traditional Chinese opera, a domain rich in cultural and visual complexity. To address this gap, we introduce the TCO-Dataset, a bilingual multimodal dataset designed to assess LLMs' ability to interpret and reason about Chinese opera images. The dataset contains 1,000 multiple-choice questions paired with high-resolution images across eight major opera genres. Each sample includes a carefully selected image, a corresponding question focused on cultural and visual understanding, and an annotated answer for evaluation. The dataset supports both Chinese and English, enabling cross-lingual model assessment. All items were reviewed through multiple rounds of expert validation to ensure consistency and accuracy. The TCO-Dataset supports diverse applications, including still-image-based visual-cultural reasoning, cultural heritage preservation, and domain-specific AI development. Initial evaluations show significant performance variation across models, underscoring the dataset's challenge and value for advancing multimodal understanding.