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arXiv · 2610.27202

The Culture Within: Towards Mechanistic Understanding of Cultural Reasoning in LLMs

Samee Arif·Furkan Danisman·Angana Borah·Honglak Lee·Dehan Kong·Lajanugen Logeswaran·Moontae Lee·Zhijing Jin·Rada Mihalcea
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Abstract

Large Language Models (LLMs) increasingly operate across culturally diverse settings, yet existing evaluations largely measure cultural behavior at the output level. We introduce CultureLens, a reference-grounded benchmark and mechanistic study of how LLMs use cultural information. CultureLens contains 9,261 scenarios across 75 countries and systematically varies country, value, true-norm, and contradictory-norm evidence. Across Llama-3.1-8B, Gemma-3-12B, and Qwen-3.5-27B, stating a contradictory norm lowers aligned answers by 32-47 percentage points, even when the model answers correctly from the country alone. Mechanistically, models default to Anglophone norms when no country is given. The failure under a contradictory norm is not one of missing knowledge, as blocking the norm inside the model restores the correct answer in most cases. Models accept a stated norm according to how it is presented and not whether it is true, which leaves them most exposed to errors in retrieved content. Supervised fine-tuning with contradictory-norm examples raises aligned answers under a contradictory norm to 85-93%, including on unseen countries, at a small cost under a true norm.

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Samee Arif, Furkan Danisman, Angana Borah, Honglak Lee, Dehan Kong, Lajanugen Logeswaran, Moontae Lee, Zhijing Jin, Rada Mihalcea. 2026-10-19. The Culture Within: Towards Mechanistic Understanding of Cultural Reasoning in LLMs. https://arxiv.org/abs/2610.27202

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