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arXiv · 2512.08646

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

Maximilian Kreutner·Jens Rupprecht·Georg Ahnert·Ahmed Salem·Markus Strohmaier
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Abstract

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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Maximilian Kreutner, Jens Rupprecht, Georg Ahnert, Ahmed Salem, Markus Strohmaier. 2025-12-09. QSTN: A Modular Framework for Robust Questionnaire Inference with Large Language Models. https://arxiv.org/abs/2512.08646

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