Cultural values alignment in Large Language Models (LLMs) is a critical challenge due to their tendency to embed Western-centric biases from training data, leading to misrepresentations and fairness issues in cross-cultural contexts. Recent approaches, such as role-assignment and few-shot learning, often struggle with reliable cultural alignment as they heavily rely on pre-trained knowledge, lack scalability, and fail to capture nuanced cultural values effectively. To address these issues, we propose ValuesRAG, a novel and effective framework that applies Retrieval-Augmented Generation (RAG) with in-context learning to integrate cultural and demographic knowledge dynamically during text generation. Leveraging the World Values Survey (WVS) dataset, ValuesRAG first generates summaries of values for each individual. Subsequently, we curated several representative regional datasets to serve as test datasets and retrieve relevant summaries of values based on demographic features, followed by a reranking step to select the top-k relevant summaries. ValuesRAG consistently outperforms baseline methods, both in the main experiment and in the ablation study where only the values summary was provided, highlighting ValuesRAG's potential to foster culturally aligned AI systems and enhance the inclusivity of AI-driven applications.

本研究解决了大型语言模型（LLMs）中文化价值对齐的关键挑战，特别是西方偏见导致的跨文化误表征和公平性问题。提出的ValuesRAG框架通过检索增强生成（RAG）和上下文学习，动态整合文化和人口统计知识，提高了文本生成中的文化对齐精度，显著优于基准方法，有助于推动文化对齐的人工智能系统的发展。

ValuesRAG：通过检索增强上下文学习提升文化对齐