AdaCultureSafe: Adaptive Cultural Safety Grounded by Cultural Knowledge in Large Language Models

arXiv:2603.08275 · cs.CL, cs.AI · Submitted 2026-03-09 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-03-09

Updated: 2026-09-08

Comments: Accepted by EMNLP 2026 Findings

Project page: https://cs.mfa.gov.cn/zggmcg/ljmdd

License: http://creativecommons.org/licenses/by/4.0/

The gist: With the global proliferation of Large Language Models (LLMs), cultural safety, defined as the ability to generate respectful and appropriate responses across diverse cultures, becomes critical for

Terminology

Abstract

With the global proliferation of Large Language Models (LLMs), cultural safety, defined as the ability to generate respectful and appropriate responses across diverse cultures, becomes critical for responsible AI deployment. However, existing research often treats cultural safety and cultural knowledge in isolation. It remains unclear whether cultural safety is grounded in understanding varying cultural knowledge to enable LLMs to adaptively yield respectful and appropriate responses across diverse cultures, which trigger ethical concerns in cross-cultural scenarios. In this work, we introduce AdaCultureSafe, a dataset designed to jointly evaluate cultural safety and knowledge. Through AdaCultureSafe, we reveal a critical insight: a significant decoupling exists between cultural safety and cultural knowledge proficiency. Although LLMs possess rich cultural knowledge, they fail to leverage it to improve cultural safety. LLMs tend to rely on generic safety rather than safety based on culture-specific knowledge. Motivated by this, we propose a knowledge-grounded method that elicits internal cultural knowledge in LLMs during response generation. Experimental results demonstrate that our approach effectively improves cultural safety. Our work can aid better understanding the landscape of cultural safety of LLMs.

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