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

2026-02-25

AI Summary

This work addresses the challenge that existing cultural alignment methods struggle to simultaneously preserve the generalized cultural values of large language models and optimize downstream task performance, often suffering from cross-cultural interference. To this end, the authors propose CultureManager, a novel framework that introduces, for the first time, a task-aware cultural data synthesis mechanism coupled with a routing-based multi-cultural adapter architecture. The approach constructs culturally relevant data via web search, synthesizes it with task-format alignment, and employs dedicated cultural adapters with dynamic routing for modular management. Extensive experiments across ten national cultures and multiple culturally sensitive tasks demonstrate that CultureManager significantly outperforms prompt engineering and fine-tuning baselines, effectively mitigating cross-cultural conflicts while enhancing task adaptability.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Humans and AI: Learning Human Values and Preferences

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• cultural alignment
• large language models
• task-specific
• cross-culture interference
• culture-sensitive tasks

Innovation

• task-aware cultural alignment
• modular culture management
• culture adapter
• culture router
• cultural data synthesis