Mind the Gap in Cultural Alignment: Task-Aware Culture Management for Large Language Models
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866915817868754944 |
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| author | Zhang, Binchi Zhao, Xujiang Li, Jundong Chen, Haifeng Chen, Zhengzhang |
| author_facet | Zhang, Binchi Zhao, Xujiang Li, Jundong Chen, Haifeng Chen, Zhengzhang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_22475 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Mind the Gap in Cultural Alignment: Task-Aware Culture Management for Large Language Models Zhang, Binchi Zhao, Xujiang Li, Jundong Chen, Haifeng Chen, Zhengzhang Computation and Language 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. |
| title | Mind the Gap in Cultural Alignment: Task-Aware Culture Management for Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2602.22475 |