Culture shapes reasoning, values, prioritization, and strategic decision-making, yet large language models (LLMs) often exhibit cultural biases that misalign with target populations. As LLMs are increasingly used for strategic decision-making, policy support, and document engineering tasks such as summarization, categorization, and compliance-oriented auditing, improving cultural alignment is important for ensuring that downstream analyses and recommendations reflect target-population value profiles rather than default model priors. Previous work introduced a survey-grounded cultural alignment framework and showed that culture-specific prompting can reduce misalignment, but it primarily evaluated proprietary models and relied on manual prompt engineering. In this paper, we validate and extend that framework by reproducing its social sciences survey based projection and distance metrics on open-weight LLMs, testing whether the same cultural skew and benefits of culture conditioning persist outside closed LLM systems. Building on this foundation, we introduce use of prompt programming with DSPy for this problem-treating prompts as modular, optimizable programs-to systematically tune cultural conditioning by optimizing against cultural-distance objectives. In our experiments, we show that prompt optimization often improves upon cultural prompt engineering, suggesting prompt compilation with DSPy can provide a more stable and transferable route to culturally aligned LLM responses.

翻译：文化塑造了推理方式、价值观念、优先级排序和战略决策，然而大型语言模型（LLMs）常表现出与目标群体文化不匹配的偏见。随着LLMs日益广泛地应用于战略决策、政策支持以及摘要生成、分类、合规性审计等文档工程任务，提升文化对齐性对于确保下游分析与建议能够反映目标群体的价值取向而非模型的默认先验至关重要。先前研究提出了基于调查的文化对齐框架，并证明特定文化提示能减少不对齐现象，但该工作主要评估了闭源模型且依赖人工提示工程。本文通过复现其社会科学调查的投影与距离度量方法，在开源权重的LLMs上验证并拓展了该框架，检验了相同的文化偏差与文化条件化优势是否在封闭LLM系统之外依然存在。在此基础上，我们引入基于DSPy的提示编程方法——将提示视为模块化、可优化的程序——通过针对文化距离目标进行优化，系统性地调整文化条件化参数。实验结果表明，提示优化通常能超越人工文化提示工程的效果，这提示采用DSPy进行提示编译可为获得文化对齐的LLM响应提供更稳定、可迁移的实现路径。