Cultural Palette: Pluralising Culture Alignment via Multi-agent Palette
Jiahao Yuan, Zixiang Di, Shangzixin Zhao, Zhiqing Cui, Hanqing Wang, Guisong Yang, Usman Naseem

TL;DR
Cultural Palette introduces a multi-agent framework inspired by cultural geography to improve large language models' alignment with diverse cultural values through adaptive, region-specific response generation and dynamic cultural blending.
Contribution
It presents a novel multi-agent approach that models cultural alignment as an adaptive color-blending process, leveraging cultural geography and GPT-4o for region-specific cultural representations.
Findings
Outperforms existing baselines in cultural alignment tasks.
Demonstrates effective region-specific response generation.
Shows robustness across diverse countries.
Abstract
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…
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Taxonomy
TopicsLanguage and cultural evolution
