Large language models (LLMs) are increasingly deployed in culturally sensitive real-world tasks. However, existing cultural alignment approaches fail to align LLMs' broad cultural values with the specific goals of downstream tasks and suffer from cross-culture interference. We propose CultureManager, a novel pipeline for task-specific cultural alignment. CultureManager synthesizes task-aware cultural data in line with target task formats, grounded in culturally relevant web search results. To prevent conflicts between cultural norms, it manages multi-culture knowledge learned in separate adapters with a culture router that selects the appropriate one to apply. Experiments across ten national cultures and culture-sensitive tasks show consistent improvements over prompt-based and fine-tuning baselines. Our results demonstrate the necessity of task adaptation and modular culture management for effective cultural alignment.
@article{arxiv.2602.22475,
title = {Mind the Gap in Cultural Alignment: Task-Aware Culture Management for Large Language Models},
author = {Binchi Zhang and Xujiang Zhao and Jundong Li and Haifeng Chen and Zhengzhang Chen},
journal= {arXiv preprint arXiv:2602.22475},
year = {2026}
}