NRITYAM: Language Models Meet Art and Heritage of Dance

2026-06-17

AI Summary

This study addresses the significant gap in current language models’ ability to understand and represent the cultural contexts of local dance traditions. To this end, the authors introduce the first large-scale benchmark for dance culture understanding, comprising 9,260 high-quality question-answer pairs across 12 languages, with content creation and validation led by indigenous dance artists and native speakers. Through systematic evaluation of large language models, small language models, and multimodal models on this benchmark, this work not only fills a critical void in assessing AI systems’ cultural comprehension within traditional performing arts but also establishes a new standard for evaluating cross-cultural competence in artificial intelligence.

Abstract

Language models have become essential tools in shaping modern workflows. However, their global effectiveness hinges on a nuanced understanding of local socio-cultural contexts. To address this gap, we present NRITYAM, a comprehensive benchmark for evaluating the cultural comprehension capabilities of language models in the context of global dance traditions. NRITYAM comprises 9,260 carefully curated question-answer pairs spanning 12 languages, making it the largest dataset dedicated to evaluating cultural knowledge in dance. The dataset has been developed from the ground up through close collaboration with native dance artists and native speakers of the languages, who authored and validated culturally relevant questions specific to their regions. We evaluate a broad set of models, including large language models, small language models, multimodal large language models, and small multimodal language models. As a multilingual and multicultural benchmark, NRITYAM sets a new standard for evaluating the ability of AI systems to understand and reason about traditional performing arts. Detailed dataset samples are available at~\url{https://github.com/niladrighosh03/NRITYAM}.

Problem

• cultural comprehension
• dance traditions
• language models
• multilingual benchmark
• performing arts

Innovation

• cultural comprehension
• multilingual benchmark
• dance heritage
• language models
• multimodal evaluation