arXiv subjects

Georg Ahnert

Publications and source records attributed to Georg Ahnert.

10 recordsLinked to original sources

arXiv2026

Total Simulated Survey Error: Designing and Diagnosing Survey Responses from Large Language Models

Large Language models (LLMs), having been trained on vast amounts of human-generated data, may encode the attitudes and behaviors of these humans. As such, LLMs show promise in mimicking human-like patterns that facilitate their use in simulating people in a wide variety of contexts. One such context is using LLMs as 'silicon samples', i.e., proxies of people in answering survey questions to establish public opinion, design policies, or use as (social) scientific data. However, several critical questions of social biases, generalization, and technical limitations remain, further complicated by a vast design space open to simulation designers. Multiverse analyses might help us make sense of the impact of different design choices, however, we lack a systematic understanding of the design space of LLM-generated surveys as well as how these decisions interplay with inherent LLM limitations. Therefore, how do we systematically identify, trace, and document limitations in LLM-generated survey responses? Building on traditions in the quantitative social sciences, specifically survey methodology and measurement theory, we investigate threats to the validity of LLM-generated survey responses. To do so, we design a framework that enumerates conceptual errors and systematic biases that can occur at different stages of the survey simulation lifecycle. Our framework, called the Total Simulated Survey Error (TS2E) Framework, provides a unified and end-to-end perspective on LLM-generated survey data. The framework, illustrated through a theoretical and empirical case study, enables survey simulation designers to systematically identify and reflect on errors in LLM-generated surveys.

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arXiv2026

In-Context Learning for the Imputation of Public Opinion Data with Large Language Models

Large language models have been widely evaluated as simulators of individual survey responses. In practice, however, fully unobserved responses are rare; the dominant problem is partial non-response. Imputation aims to restore the overall structure of a survey dataset by filling in these missing values. It has its own well-defined evaluation criteria and differs fundamentally from prediction. We propose to impute missing survey data through in-context learning (ICL). We systematically evaluate ICL design choices across different missingness mechanisms (MCAR, MAR, MNAR) on 150 opinion variables spanning 15 waves of the American Trends Panel. Compared to well-established statistical methods for data imputation like MICE PMM, our ICL approach consistently reduces absolute error across all missingness mechanisms, with the largest gains under non-random missingness (MNAR). Notably, the best-performing specification (gpt-oss-120b with 100 in-context examples) achieves near-nominal aggregate coverage (approaching the 95% level) with confidence intervals two to five times narrower than MICE PMM. We publish a Python package with an sklearn-like API to enable easy deployment of our method using local and proprietary LLMs.

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arXiv2025

QSTN: A Modular Framework for Robust Questionnaire Inference with Large Language Models

We introduce QSTN, an open-source Python framework for systematically generating responses from questionnaire-style prompts to support in-silico surveys and annotation tasks with large language models (LLMs). QSTN enables robust evaluation of questionnaire presentation, prompt perturbations, and response generation methods. Our extensive evaluation (>40 million survey responses) shows that question structure and response generation methods have a significant impact on the alignment of generated survey responses with human answers. We also find that answers can be obtained for a fraction of the compute cost, by changing the presentation method. In addition, we offer a no-code user interface that allows researchers to set up robust experiments with LLMs \emph{without coding knowledge}. We hope that QSTN will support the reproducibility and reliability of LLM-based research in the future.

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arXiv2025

Survey Response Generation: Generating Closed-Ended Survey Responses In-Silico with Large Language Models

Many in-silico simulations of human survey responses with large language models (LLMs) focus on generating closed-ended survey responses, whereas LLMs are typically trained to generate open-ended text instead. Previous research has used a diverse range of methods for generating closed-ended survey responses with LLMs, and a standard practice remains to be identified. In this paper, we systematically investigate the impact that various Survey Response Generation Methods have on predicted survey responses. We present the results of 32 mio. simulated survey responses across 8 Survey Response Generation Methods, 4 political attitude surveys, and 10 open-weight language models. We find significant differences between the Survey Response Generation Methods in both individual-level and subpopulation-level alignment. Our results show that Restricted Generation Methods perform best overall, and that reasoning output does not consistently improve alignment. Our work underlines the significant impact that Survey Response Generation Methods have on simulated survey responses, and we develop practical recommendations on the application of Survey Response Generation Methods.

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arXiv2025

The Prompt Makes the Person(a): A Systematic Evaluation of Sociodemographic Persona Prompting for Large Language Models

Persona prompting is increasingly used in large language models (LLMs) to simulate views of various sociodemographic groups. However, how a persona prompt is formulated can significantly affect outcomes, raising concerns about the fidelity of such simulations. Using five open-source LLMs, we systematically examine how different persona prompt strategies, specifically role adoption formats and demographic priming strategies, influence LLM simulations across 15 intersectional demographic groups in both open- and closed-ended tasks. Our findings show that LLMs struggle to simulate marginalized groups but that the choice of demographic priming and role adoption strategy significantly impacts their portrayal. Specifically, we find that prompting in an interview-style format and name-based priming can help reduce stereotyping and improve alignment. Surprisingly, smaller models like OLMo-2-7B outperform larger ones such as Llama-3.3-70B. Our findings offer actionable guidance for designing sociodemographic persona prompts in LLM-based simulation studies.

cs.CL↗

arXiv2025

Prompt Perturbations Reveal Human-Like Biases in Large Language Model Survey Responses

Large Language Models (LLMs) are increasingly used as proxies for human subjects in social science surveys, but their reliability and susceptibility to known human-like response biases, such as central tendency, opinion floating and primacy bias are poorly understood. This work investigates the response robustness of LLMs in normative survey contexts, we test nine LLMs on questions from the World Values Survey (WVS), applying a comprehensive set of ten perturbations to both question phrasing and answer option structure, resulting in over 167,000 simulated survey interviews. In doing so, we not only reveal LLMs' vulnerabilities to perturbations but also show that all tested models exhibit a consistent recency bias, 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. This underscores the critical importance of prompt design and robustness testing when using LLMs to generate synthetic survey data.

cs.CL↗

arXiv2025

Simulating Persuasive Dialogues on Meat Reduction with Generative Agents

Meat reduction benefits human and planetary health, but social norms keep meat central in shared meals. To date, the development of communication strategies that promote meat reduction while minimizing social costs has required the costly involvement of human participants at each stage of the process. We present work in progress on simulating multi-round dialogues on meat reduction between Generative Agents based on large language models (LLMs). We measure our main outcome using established psychological questionnaires based on the Theory of Planned Behavior and additionally investigate Social Costs. We find evidence that our preliminary simulations produce outcomes that are (i) consistent with theoretical expectations; and (ii) valid when compared to data from previous studies with human participants. Generative agent-based models are a promising tool for identifying novel communication strategies on meat reduction -- tailored to highly specific participant groups -- to then be tested in subsequent studies with human participants.

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arXiv2024

Extracting Affect Aggregates from Longitudinal Social Media Data with Temporal Adapters for Large Language Models

This paper proposes temporally aligned Large Language Models (LLMs) as a tool for longitudinal analysis of social media data. We fine-tune Temporal Adapters for Llama 3 8B on full timelines from a panel of British Twitter users, and extract longitudinal aggregates of emotions and attitudes with established questionnaires. We focus our analysis on the beginning of the COVID-19 pandemic that had a strong impact on public opinion and collective emotions. We validate our estimates against representative British survey data and find strong positive, significant correlations for several collective emotions. The obtained estimates are robust across multiple training seeds and prompt formulations, and in line with collective emotions extracted using a traditional classification model trained on labeled data. We demonstrate the flexibility of our method on questions of public opinion for which no pre-trained classifier is available. Our work extends the analysis of affect in LLMs to a longitudinal setting through Temporal Adapters. It enables flexible, new approaches towards the longitudinal analysis of social media data.

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arXiv2024

Fair Pairs: Fairness-Aware Ranking Recovery from Pairwise Comparisons

Pairwise comparisons based on human judgements are an effective method for determining rankings of items or individuals. However, as human biases perpetuate from pairwise comparisons to recovered rankings, they affect algorithmic decision making. In this paper, we introduce the problem of fairness-aware ranking recovery from pairwise comparisons. We propose a group-conditioned accuracy measure which quantifies fairness of rankings recovered from pairwise comparisons. We evaluate the impact of state-of-the-art ranking recovery algorithms and sampling approaches on accuracy and fairness of the recovered rankings, using synthetic and empirical data. Our results show that Fairness-Aware PageRank and GNNRank with FA*IR post-processing effectively mitigate existing biases in pairwise comparisons and improve the overall accuracy of recovered rankings. We highlight limitations and strengths of different approaches, and provide a Python package to facilitate replication and future work on fair ranking recovery from pairwise comparisons.

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arXiv2021

The FairCeptron: A Framework for Measuring Human Perceptions of Algorithmic Fairness

Measures of algorithmic fairness often do not account for human perceptions of fairness that can substantially vary between different sociodemographics and stakeholders. The FairCeptron framework is an approach for studying perceptions of fairness in algorithmic decision making such as in ranking or classification. It supports (i) studying human perceptions of fairness and (ii) comparing these human perceptions with measures of algorithmic fairness. The framework includes fairness scenario generation, fairness perception elicitation and fairness perception analysis. We demonstrate the FairCeptron framework by applying it to a hypothetical university admission context where we collect human perceptions of fairness in the presence of minorities. An implementation of the FairCeptron framework is openly available, and it can easily be adapted to study perceptions of algorithmic fairness in other application contexts. We hope our work paves the way towards elevating the role of studies of human fairness perceptions in the process of designing algorithmic decision making systems.

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