Mind the Gap in Cultural Alignment: Task-Aware Culture Management for Large Language Models

Publication Date: 7/7/2026

Event: The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)

Reference: 16798–16813

Authors: Binchi Zhang, University of Virginia; Zhengzhang Chen, NEC Laboratories America, Inc.; Xujiang Zhao, NEC Laboratories America, Inc.; Jundong Li, University of Virginia; Haifeng Chen, NEC Laboratories America, Inc.

Abstract: Large language models (LLMs) are increasingly deployed in culturally sensitive real-world tasks. However, existing cultural alignment ap-roaches 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 five national cultures and ten 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.

Publication Link: https://aclanthology.org/2026.acl-long.766/