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

TL;DR

This paper introduces CultureManager, a task-aware cultural alignment pipeline for large language models that manages multi-cultural knowledge to improve performance on culturally sensitive tasks.

Contribution

The paper presents a novel modular approach for task-specific cultural alignment in LLMs, addressing cross-culture interference and enhancing cultural sensitivity.

Findings

Consistent improvements over prompt-based and fine-tuning baselines.

Effective management of multi-culture knowledge with a culture router.

Demonstrates the importance of task adaptation for cultural alignment.

Abstract

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.