基于德国社会调查构建可复用的虚拟人物模板，提升大模型模拟真实人群的准确性。
German General Social Survey Personas: A Survey-Derived Persona Prompt Collection for Population-Aligned LLM Studies
- 从德国全国社会调查数据中提取代表性人物特征，生成可直接用于大模型的提示词。
- 在数据稀缺时，使用该模板的模型更接近真实人口回答分布，表现优于现有分类器。
- 适合从事社会模拟、群体行为建模或需代表性强的人类视角研究的学者使用。
利用大语言模型（LLMs）通过角色提示模拟人类观点在计算社会科学中日益流行。然而，高质量、基于实证的角色提示集合仍十分稀缺，限制了模拟的准确性和代表性。本文提出德国全国社会调查人物集（GGSS Personas），一个基于德国全国社会调查（ALLBUS）构建的全面且具有代表性的角色提示集合。该集合设计为可直接适配各类大模型与任务，引导模型生成与德国总体人口一致的响应。我们通过让不同大模型使用该提示模拟多个议题的调查回答分布进行评估，结果表明，使用GGSS Personas的大模型在数据稀缺条件下表现显著优于当前最优分类器。此外，我们分析了角色提示中代表性和属性选择对与真实人口回应对齐的影响。研究发现，GGSS Personas为基于大模型的社会模拟研究提供了宝贵资源，有助于在自然语言处理与社会科学中系统开展人口对齐的角色提示探索。
原文摘要 · Abstract (English)
The use of Large Language Models (LLMs) for simulating human perspectives via persona prompting is gaining traction in computational social science. However, well-curated, empirically grounded persona collections remain scarce, limiting the accuracy and representativeness of such simulations. Here, we introduce the German General Social Survey Personas (GGSS Personas) collection, a comprehensive and representative persona prompt collection built from the German General Social Survey (ALLBUS). The GGSS Personas and their persona prompts are designed to be easily plugged into prompts for all types of LLMs and tasks, steering models to generate responses aligned with the underlying German population. We evaluate GGSS Personas by prompting various LLMs to simulate survey response distributions across diverse topics, demonstrating that GGSS Personas-guided LLMs outperform state-of-the-art classifiers, particularly under data scarcity. Furthermore, we analyze how the representativity and attribute selection within persona prompts affect alignment with population responses. Our findings suggest that GGSS Personas provide a potentially valuable resource for research on LLM-based social simulations that enables more systematic explorations of population-aligned persona prompting in NLP and social science research.
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