German General Social Survey Personas: A Survey-Derived Persona Prompt Collection for Population-Aligned LLM Studies

TL;DR

This paper introduces the GGSS Personas, a collection of empirically grounded, population-representative persona prompts derived from the German General Social Survey, to improve LLM-based social simulations and response alignment.

Contribution

The paper presents the first comprehensive, survey-derived persona prompt collection for LLMs based on a large-scale social survey, enhancing population-aligned social simulations.

Findings

GGSS Personas improve LLM response accuracy across topics.

They outperform state-of-the-art classifiers, especially with limited data.

Representativity and attribute selection influence response alignment.

Abstract

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.