Nunchi-Bench: Benchmarking Language Models on Cultural Reasoning with a Focus on Korean Superstition
This addresses the need for culturally sensitive AI in multicultural environments, though it is incremental as it focuses on a specific domain and benchmark.
The authors tackled the problem of evaluating large language models' cultural reasoning, specifically for Korean superstitions, by introducing Nunchi-Bench with 247 questions across 31 topics, finding that models struggle to apply factual knowledge in practical scenarios and that explicit cultural framing improves performance more than language alone.
As large language models (LLMs) become key advisors in various domains, their cultural sensitivity and reasoning skills are crucial in multicultural environments. We introduce Nunchi-Bench, a benchmark designed to evaluate LLMs' cultural understanding, with a focus on Korean superstitions. The benchmark consists of 247 questions spanning 31 topics, assessing factual knowledge, culturally appropriate advice, and situational interpretation. We evaluate multilingual LLMs in both Korean and English to analyze their ability to reason about Korean cultural contexts and how language variations affect performance. To systematically assess cultural reasoning, we propose a novel evaluation strategy with customized scoring metrics that capture the extent to which models recognize cultural nuances and respond appropriately. Our findings highlight significant challenges in LLMs' cultural reasoning. While models generally recognize factual information, they struggle to apply it in practical scenarios. Furthermore, explicit cultural framing enhances performance more effectively than relying solely on the language of the prompt. To support further research, we publicly release Nunchi-Bench alongside a leaderboard.