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        <title>German General Social Survey Personas: A Survey-Derived Persona Prompt Collection for Population-Aligned LLM Studies</title>
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    <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 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.</abstract>
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%0 Conference Proceedings
%T German General Social Survey Personas: A Survey-Derived Persona Prompt Collection for Population-Aligned LLM Studies
%A Rupprecht, Jens
%A Froehling, Leon
%A Wagner, Claudia
%A Strohmaier, Markus
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F rupprecht-etal-2026-german
%X 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 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.
%R 10.63317/2sod6uekicbg
%U https://aclanthology.org/2026.lrec-1.139/
%U https://doi.org/10.63317/2sod6uekicbg
%P 1761-1780

Markdown (Informal)

[German General Social Survey Personas: A Survey-Derived Persona Prompt Collection for Population-Aligned LLM Studies](https://aclanthology.org/2026.lrec-1.139/) (Rupprecht et al., LREC 2026)

• German General Social Survey Personas: A Survey-Derived Persona Prompt Collection for Population-Aligned LLM Studies (Rupprecht et al., LREC 2026)

ACL

• Jens Rupprecht, Leon Froehling, Claudia Wagner, and Markus Strohmaier. 2026. German General Social Survey Personas: A Survey-Derived Persona Prompt Collection for Population-Aligned LLM Studies. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 1761–1780, Palma de Mallorca, Spain. ELRA Language Resource Association.