mlsanityData to 7 Oct 2026

Let's Play Across Cultures: A Large Multilingual, Multicultural Benchmark for Assessing Language Models' Understanding of Sports

arXiv 2510.01247 ↗First posted 24 Sep 2025cs.CL cs.AIcited by 3

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Abstract (arXiv)

Language Models (LMs) are primarily evaluated on globally popular sports, often overlooking regional and indigenous sporting traditions. To address this gap, we introduce textbftextitCultSportQA, a benchmark designed to assess LMs' understanding of traditional sports across 60 countries and 6 continents, encompassing four distinct cultural categories. The dataset features 33,000 multiple-choice questions (MCQs) across text and image modalities, each of which is categorized into three key types: history-based, rule-based, and scenario-based. To evaluate model performance, we employ zero-shot, few-shot, and chain-of-thought (CoT) prompting across a diverse set of Large Language Models (LLMs), Small Language Models (SLMs), and Multimodal Large Language Models (MLMs). By providing a comprehensive multilingual and multicultural sports benchmark, textbftextitCultSportQA establishes a new standard for assessing AI's ability to understand and reason about traditional sports.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 33 of its 62 references that are in the map. First posted on arXiv 24 Sep 2025.

Builds on

33 of 62 references.

References · 33

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025CLINIC: Evaluating Multilingual Trustworthiness in Language Models for Healthcarecited by 7
  2. 2026NRITYAM: Language Models Meet Art and Heritage of Dancecited by 0
  3. 2026CultureMINE: Datasets and Methods for Improving the Cultural Capabilities of NLP Systemscited by 0

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  3. 2024Sports Intelligence: Assessing the Sports Understanding Capabilities of Language Models through Question Answering from Text to Video
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  8. 2025Grounding Multilingual Multimodal LLMs With Cultural Knowledge