MyCulture: Exploring Malaysia's Diverse Culture under Low-Resource Language Constraints
This addresses the challenge of accurately representing diverse cultural contexts in low-resource language settings for LLM developers and researchers, though it is incremental as it builds on existing benchmark methods.
The authors tackled the problem of cultural biases in Large Language Models (LLMs) by introducing MyCulture, a benchmark for evaluating LLMs on Malaysian culture in Bahasa Melayu, which revealed significant disparities in cultural comprehension across models.
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.