Nunchi-Bench: Benchmarking Language Models on Cultural Reasoning with a Focus on Korean Superstition

ACL ARR 2025 February Submission4262 Authors

Abstract:

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 verification 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.

Paper Type: Long

Research Area: Computational Social Science and Cultural Analytics

Research Area Keywords: language/cultural bias analysis

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Data resources, Data analysis

Languages Studied: English, Korean

Submission Number: 4262