The “Knowledge–Behavior Gap” in Cultural Taboo Safety of Large Language Models

Ying He, Sihang Jiang, Xingzhou Chen, Zhouhong Gu, Yiwei Gu, Minggui HE, Shimin Tao, Mahongxia, Yanghua Xiao


Abstract
Cultural taboo safety is essential for deploying large language models (LLMs), as culturally insensitive outputs may cause offense or even social harm. However, existing cultural benchmarks primarily assess cultural knowledge or values biases, while overlooking whether LLMs can recognize and respect cultural taboos, especially when taboos are implicitly hidden in seemingly harmless questions. Besides, cultural taboos are implicit, and context-dependent, thus poss unique challenges for reliable evaluation. To address these gaps, we introduce **CulShield**, the first public benchmark dedicated to evaluating and improving the cultural taboo safety of LLMs. CulShield spans 77 countries and regions, and includes over 2,020 taboos. It evaluates models along both explicit knowledge and implicit behaviors.Experiments on several advanced LLMs (e.g., GPT-4o-mini, Gemini-2.5-pro) reveal a clear "knowledge-behavior gap": models often fail to apply known taboos during interaction. We further show that variations in linguistic context can significantly affect LLMs’ cultural taboo safety. Code and data is accessible here: https://anonymous.4open.science/r/CulShield-7A0E.
Anthology ID:
2026.acl-long.1424
Volume:
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2026
Address:
San Diego, California, United States
Editors:
Maria Liakata, Viviane P. Moreira, Jiajun Zhang, David Jurgens
Venue:
ACL
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Publisher:
Association for Computational Linguistics
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Pages:
30846–30866
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URL:
https://preview.aclanthology.org/ingest-acl/2026.acl-long.1424/
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Cite (ACL):
Ying He, Sihang Jiang, Xingzhou Chen, Zhouhong Gu, Yiwei Gu, Minggui HE, Shimin Tao, Mahongxia, and Yanghua Xiao. 2026. The “Knowledge–Behavior Gap” in Cultural Taboo Safety of Large Language Models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 30846–30866, San Diego, California, United States. Association for Computational Linguistics.
Cite (Informal):
The “Knowledge–Behavior Gap” in Cultural Taboo Safety of Large Language Models (He et al., ACL 2026)
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https://preview.aclanthology.org/ingest-acl/2026.acl-long.1424.pdf
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