德国通用社会调查人设：一种基于德国通用社会调查的人员提示集合，用于与人口一致的LLM研究
German General Social Survey Personas: A Survey-Derived Persona Prompt Collection for Population-Aligned LLM Studies
AI总结：
本文提出德国通用社会调查人设集合，用于构建与人口一致的LLM研究，通过实验证明其在模拟调查响应方面的有效性。
摘要：
利用大型语言模型（LLMs）通过人设提示模拟人类视角在计算社会科学中越来越受到关注。然而，经过精心整理、基于实证的人设集合仍然稀缺，限制了此类模拟的准确性和代表性。在这里，我们介绍了德国通用社会调查人设（GGSS Personas）集合，这是一个全面且具有代表性的人员提示集合，基于德国通用社会调查（ALLBUS）构建。GGSS Personas及其人设提示旨在轻松嵌入到各种LLM和任务的提示中，引导模型生成与底层德国人口一致的响应。我们通过提示各种LLM在不同主题上模拟调查响应分布来评估GGSS Personas，证明了在数据稀缺情况下，由GGSS Personas引导的LLM优于最先进的分类器。此外，我们分析了人设提示中代表性和属性选择如何影响与人口响应的一致性。我们的发现表明，GGSS Personas为基于LLM的社会模拟研究提供了一种潜在有价值的资源，能够更系统地探索自然语言处理和社会科学研究中与人口一致的人设提示。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
