MyCulture: Exploring Malaysia's Diverse Culture under Low-Resource Language Constraints

• URL: http://arxiv.org/abs/2508.05429v2
• Date: Fri, 08 Aug 2025 01:24:20 GMT
• Title: MyCulture: Exploring Malaysia's Diverse Culture under Low-Resource Language Constraints
• Authors: Zhong Ken Hew, Jia Xin Low, Sze Jue Yang, Chee Seng Chan,
• Abstract summary: MyCulture is a benchmark designed to comprehensively evaluate Large Language Models (LLMs) on Malaysian culture.<n>Unlike conventional benchmarks, MyCulture employs a novel open-ended multiple-choice question format without predefined options.<n>We analyze structural bias by comparing model performance on structured versus free-form outputs, and assess language bias through multilingual prompt variations.
• Score: 7.822567458977689
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) often exhibit cultural biases due to training data dominated by high-resource languages like English and Chinese. This poses challenges for accurately representing and evaluating diverse cultural contexts, particularly in low-resource language settings. To address this, we introduce MyCulture, a benchmark designed to comprehensively evaluate LLMs on Malaysian culture across six pillars: arts, attire, customs, entertainment, food, and religion presented in Bahasa Melayu. Unlike conventional benchmarks, MyCulture employs a novel open-ended multiple-choice question format without predefined options, thereby reducing guessing and mitigating format bias. We provide a theoretical justification for the effectiveness of this open-ended structure in improving both fairness and discriminative power. Furthermore, we analyze structural bias by comparing model performance on structured versus free-form outputs, and assess language bias through multilingual prompt variations. Our evaluation across a range of regional and international LLMs reveals significant disparities in cultural comprehension, highlighting the urgent need for culturally grounded and linguistically inclusive benchmarks in the development and assessment of LLMs.

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