当大型语言模型生成真实的社会网络时？文化、语言、规模和方法的多维研究
When Do LLMs Generate Realistic Social Networks? A Multi-Dimensional Study of Culture, Language, Scale, and Method
AI总结：
本文通过多维度研究探讨LLM生成社会网络的可靠性，发现文化框架、提示语言和模型规模等因素显著影响网络结构，揭示提示设计对社会学假设的影响。
摘要：
大型语言模型（LLMs）日益被用作人类主体的替代品，在行为模拟中包括合成社会网络生成。然而，其关系输出如何依赖于提示设计、文化框架、提示语言和模型规模仍不清楚。基于同质性理论和结构平衡理论，我们正式化了四种LLM基于的纽带形成机制：顺序、全局、局部和迭代，并将其视为边缘集上的不同条件分布。使用固定名单的50个具有人口统计数据的个人，我们生成了192个经过验证的有向网络，涵盖四种文化背景、四种提示语言、三种GPT-4.1变体和四种提示架构，每个条件有两个种子。我们发现文化框架会改变近亲同质性和最大组件连通性。政治倾向在三种方法下主导纽带形成，而全局方法替代年龄，显示提示架构作为实质性的社会学变量。模型规模产生稳定的分歧排名，最小变体在定性上表现不同而非仅噪声。提示语言单独显著改变宗教同质性，尤其是在印地语提示下，而政治同质性几乎不变。LLM生成的网络在聚类和模块度上比标准图基线更接近真实社会图，但编码了高于经验水平的种族偏见。这些结果表明，提示选择常被视为实现细节，却编码了实质性的社会学假设。
英文摘要：
Large language models (LLMs) are increasingly used as substitutes for human subjects in behavioral simulations, including synthetic social network generation. Yet it remains unclear how their relational outputs depend on prompt design, cultural framing, prompt language, and model scale. Building on homophily theory and structural balance theory, we formalize four LLM-based tie-formation mechanisms: sequential, global, local, and iterative, and treat them as distinct conditional distributions over edge sets. Using a fixed roster of 50 demographically grounded personas, we generate 192 verified directed networks across four cultural contexts, four prompt languages, three GPT-4.1 variants, and four prompting architectures, with two seeds per condition. We find that cultural framing shifts inbreeding homophily and largest-component connectivity. Political affiliation dominates tie formation under three methods, while the global method substitutes age, showing that prompt architecture functions as a substantive sociological variable. Model scale produces a stable divergence ranking, with the smallest variant behaving qualitatively differently rather than merely noisily. Prompt language alone sharply shifts religion homophily, especially under Hindi prompting, while leaving political homophily nearly invariant. LLM-generated networks match real social graphs on clustering and modularity better than standard graph baselines, yet encode demographic biases above empirical levels. These results show that prompt choices often treated as implementation details encode substantive sociological assumptions.
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