Hi, I'm Jens!

University of Mannheim

L 15, 1–6

68161 Mannheim

I am a PhD student at the Chair for Data Science in the Economic and Social Sciences at the University of Mannheim. Our chair focuses on developing methods for understanding human social and economic behavior via analyzing textual and relational data.

My academic background bridges two distinct but complementary fields, with a B.A. in Sociology from Otto-Friedrich University Bamberg and an M.Sc. in Data Science from the University of Mannheim. This combination drives my research in Computational Social Science, where I focus on the deployment and evaluation of Large Language Models. Specifically, I am interested in two key areas: leveraging and evaluating LLMs for synthetic data generation to model society, and advancing knowledge in survey methodology. I pursue computational, empirical and data-driven research approaches in collaboration with colleagues from the computer sciences, the economic sciences and the social sciences.

news

Jun 30, 2025	I will be participating at IC²S² Conference in Norrköping, Sweden from the 21. to the 24. July 2025 presenting a poster of the preprint: Prompt Perturbations Reveal Human-Like Biases in LLM Survey Responses

selected publications

Prompt Perturbations Reveal Human-Like Biases in LLM Survey Responses

Jens Rupprecht, Georg Ahnert, and Markus Strohmaier

Jul 2025

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Large Language Models (LLMs) are increasingly used as proxies for human subjects in social science surveys, but their reliability and susceptibility to known response biases are poorly understood. This paper investigates the response robustness of LLMs in normative survey contexts – we test nine diverse LLMs on questions from the World Values Survey (WVS), applying a comprehensive set of 11 perturbations to both question phrasing and answer option structure, resulting in over 167,000 simulated interviews. In doing so, we not only reveal LLMs’ vulnerabilities to perturbations but also reveal that all tested models exhibit a consistent }textit{recency bias} varying in intensity, disproportionately favoring the last-presented answer option. While larger models are generally more robust, all models remain sensitive to semantic variations like paraphrasing and to combined perturbations. By applying a set of perturbations, we reveal that LLMs partially align with survey response biases identified in humans. This underscores the critical importance of prompt design and robustness testing when using LLMs to generate synthetic survey data.
"Charging Process Chain"

Felix Röckle, Marco Raul Soares Amorim, Lukas Keicher, and 2 more authors

Jul 2022

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As part of the "Wirkkette Laden" funding project, a holistic approach for analysing weak points in the charging ecosystem was developed and applied in collaboration between research and industry. The approach is based on four building blocks: Statistical data analysis, a diary study, a charging experiment, and an AI-based analysis of online user comments. The results show that there are challenges both in terms of the individual components of the charging ecosystem, such as charging stations, and in terms of the interfaces between the components within the system.