mlsanityData to 14 Oct 2026

NRITYAM: Language Models Meet Art and Heritage of Dance

arXiv 2606.19727 ↗First posted 18 Jun 2026cs.CL cs.AIcited by 0

On the map: Multimodal LLMs / vision-language models → VLM benchmarks and visual reasoning

Abstract (arXiv)

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~urlhttps://github.com/niladrighosh03/NRITYAM.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 30 of its 47 references that are in the map. First posted on arXiv 18 Jun 2026.

Builds on

30 of 47 references.

Closest ancestors · 6

Same direction, newest first

  1. 2024All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages25 Nov
  2. 2024WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines16 Oct
  3. 2024Benchmarking Vision Language Models for Cultural Understanding15 Jul
  4. 2024PaliGemma: A versatile 3B VLM for transfer10 Jul
  5. 2024CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark10 Jun
  6. 2023mBLIP: Efficient Bootstrapping of Multilingual Vision-LLMs13 Jul

Other references · 24

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  11. 2023Multilingual Jailbreak Challenges in Large Language Modelscited by 240
  12. 2021Visually Grounded Reasoning across Languages and Culturescited by 189
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  1. 2025DeepSeek-OCR: Contexts Optical Compressioncited by 183
  2. 2024Investigating Cultural Alignment of Large Language Modelscited by 143
  3. 2022The Ghost in the Machine has an American accent: value conflict in GPT-3cited by 105
  4. 2024MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answeringcited by 93
  5. 2015Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answeringcited by 80
  6. 2021xGQA: Cross-Lingual Visual Question Answeringcited by 72
  7. 2023Multi-lingual and Multi-cultural Figurative Language Understandingcited by 47
  8. 2024Evaluating Cultural Adaptability of a Large Language Model via Simulation of Synthetic Personascited by 37
  9. 2023Are Multilingual LLMs Culturally-Diverse Reasoners? An Investigation into Multicultural Proverbs and Sayingscited by 32
  10. 2024FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food Culturecited by 25
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  12. 2025Let's Play Across Cultures: A Large Multilingual, Multicultural Benchmark for Assessing Language Models' Understanding of Sportscited by 3

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