让大模型按任务需求调整文化表达，避免跨文化冲突。
Mind the Gap in Cultural Alignment: Task-Aware Culture Management for Large Language Models
- 根据任务格式生成特定文化数据，动态选择适配的文化模块。
- 在10个不同国家文化任务中，显著优于提示和微调基线。
- 适合需要跨文化精准响应的AI应用，如国际客服、内容生成。
大型语言模型在文化敏感型实际任务中日益普及，但现有文化对齐方法无法将广泛文化价值观与下游任务目标对齐，且易受跨文化干扰。本文提出CultureManager，一种面向任务的文化对齐新流程。该方法基于文化相关的网络搜索结果，生成符合目标任务格式的任务感知文化数据；为避免文化规范间的冲突，采用文化路由机制，从独立适配器中选择合适文化知识进行应用。在涵盖十个国家文化的多个文化敏感任务上实验表明，其性能持续优于基于提示和微调的基线方法。结果证明，任务适应性和模块化文化管理对于有效文化对齐至关重要。
原文摘要 · Abstract (English)
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.
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