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

Singh, Punit Kumar; Kumar, Nishant; Ghosh, Akash; Pasad, Kunal; Soni, Khushi; Jaishwal, Manisha; Saha, Sriparna; Alfarozi, Syukron Abu Ishaq; Abagissa, Asres Temam; Pasupa, Kitsuchart; Yang, Haiqin; Moreno, Jose G.

doi:10.18653/v1/2025.emnlp-main.769

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

Author(s)

Singh, Punit Kumar

Kumar, Nishant

Ghosh, Akash

Pasad, Kunal

Soni, Khushi

Jaishwal, Manisha

Saha, Sriparna

Alfarozi, Syukron Abu Ishaq

Abagissa, Asres Temam

Pasupa, Kitsuchart

Yang, Haiqin

Moreno, Jose G.

Date Issued

January 1, 2025

Type

Conference Paper

DOI

10.18653/v1/2025.emnlp-main.769

Abstract

Language Models (LMs) are primarily evaluated on globally popular sports, often overlooking regional and indigenous sporting traditions. To address this gap, we introduce CultSportQA, 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, CultSportQA establishes a new standard for assessing AI's ability to understand and reason about traditional sports.

Citation

Emnlp 2025 2025 Conference on Empirical Methods in Natural Language Processing Proceedings of the Conference, 15194-15241, 2025