@inproceedings{kreutner-etal-2026-qstn,
title = "{QSTN}: A Modular Framework for Robust Questionnaire Inference with Large Language Models",
author = "Kreutner, Maximilian and
Rupprecht, Jens and
Ahnert, Georg and
Salem, Ahmed and
Strohmaier, Markus",
editor = "Croce, Danilo and
Leidner, Jochen and
Moosavi, Nafise Sadat",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
month = mar,
year = "2026",
address = "Rabat, Marocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-demo.37/",
pages = "537--549",
ISBN = "979-8-89176-382-1",
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 ({\ensuremath{>}}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 without coding knowledge. We hope that QSTN will support the reproducibility and reliability of LLM-based research in the future."
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%0 Conference Proceedings
%T QSTN: A Modular Framework for Robust Questionnaire Inference with Large Language Models
%A Kreutner, Maximilian
%A Rupprecht, Jens
%A Ahnert, Georg
%A Salem, Ahmed
%A Strohmaier, Markus
%Y Croce, Danilo
%Y Leidner, Jochen
%Y Moosavi, Nafise Sadat
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Marocco
%@ 979-8-89176-382-1
%F kreutner-etal-2026-qstn
%X 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 (\ensuremath>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 without coding knowledge. We hope that QSTN will support the reproducibility and reliability of LLM-based research in the future.
%U https://aclanthology.org/2026.eacl-demo.37/
%P 537-549
Markdown (Informal)
[QSTN: A Modular Framework for Robust Questionnaire Inference with Large Language Models](https://aclanthology.org/2026.eacl-demo.37/) (Kreutner et al., EACL 2026)
ACL