Large language models (LLMs) face challenges in aligning with diverse cultural values despite their remarkable performance in generation, which stems from inherent monocultural biases and difficulties in capturing nuanced cultural semantics. Existing methods struggle to adapt to unknown culture after fine-tuning. Inspired by cultural geography across five continents, we propose Cultural Palette, a multi-agent framework that redefines cultural alignment as an adaptive "color-blending" process for country-specific adaptation. Our approach harnesses cultural geography across five continents through three key steps: First, we synthesize the Pentachromatic Cultural Palette Dataset using GPT-4o, refining continental-level dialogues with Hofstede's cultural dimensions to establish foundational cultural representations. Second, five continent-level alignment agents form specialized cultural communities that generate region-specific draft responses. Third, a Meta Agent employs Cultural MoErges to dynamically blend these cultural "colors" through attention-gated parameter merging, akin to mixing pigments on a palette, resolving conflicts while preserving cultural nuances to produce the final culturally-aligned response. Extensive experiments across various countries demonstrate that \textit{Cultural Palette} surpasses existing baselines in cultural alignment.

翻译：尽管大型语言模型在生成任务中表现出色，但其固有的单一文化偏见和捕捉细微文化语义的困难，使其在适应多元文化价值观方面面临挑战。现有方法在微调后难以适应未知文化。受五大洲文化地理学启发，我们提出文化调色板——一个将文化对齐重新定义为针对特定国家适应的动态“色彩融合”过程的多智能体框架。该方法通过三个关键步骤整合五大洲的文化地理特征：首先，我们利用GPT-4o合成五色谱文化调色板数据集，结合霍夫斯泰德文化维度理论精炼大洲级对话，建立基础文化表征。其次，五个大洲级对齐智能体形成专业化文化社群，生成针对特定区域的草稿响应。第三，元智能体采用文化混合专家机制，通过注意力门控参数融合动态调配这些文化“色彩”——如同在调色板上混合颜料——在保留文化细微特征的同时解决冲突，最终生成文化对齐的响应。跨多国的广泛实验表明，\textit{Cultural Palette}在文化对齐方面超越了现有基线方法。