@inproceedings{omiecienski-etal-2026-prompt,
title = "Prompt-Based Stance Control in {G}erman: An Evaluation of {LLM}s for Experimental Research on Attitude Change",
author = "Omiecienski, Florian and
Sindermann, Cornelia and
Falenska, Agnieszka",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.644/",
doi = "10.63317/2tvnax68shcy",
pages = "8122--8140",
abstract = "How much can Large Language Models (LLMs) influence the attitudes and opinions of their users? Answering this question requires controlled pre/post-treatment experiments, where participants interact with LLMs that consistently adopt a predefined political stance. Such experiments, however, are only possible if LLMs can be reliably steered to hold these stances throughout the interactions. In this work, we evaluate whether state-of-the-art LLMs can be effectively stance-controlled in German, thereby enabling experiments on human{--}LLM interactions. First, using a corpus of realistic user prompts, we find that LLMs are predominantly neutral, making them infeasible for said experiments. We then show that a prompt-based stance control method can reliably guide models to argue for or against a particular topic. Finally, we analyze confounding factors like topic and stance of the initial user prompts. We find that control is easiest when the target stance aligns with topical priors of the model or a user{'}s prompt. Further, the models maintain a comparable style across target stances {---} a key prerequisite for pre/post-treatment experiments. Taken together, our results demonstrate that stance-controlled LLMs are feasible and practically useful for experiments on user attitude change."
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<abstract>How much can Large Language Models (LLMs) influence the attitudes and opinions of their users? Answering this question requires controlled pre/post-treatment experiments, where participants interact with LLMs that consistently adopt a predefined political stance. Such experiments, however, are only possible if LLMs can be reliably steered to hold these stances throughout the interactions. In this work, we evaluate whether state-of-the-art LLMs can be effectively stance-controlled in German, thereby enabling experiments on human–LLM interactions. First, using a corpus of realistic user prompts, we find that LLMs are predominantly neutral, making them infeasible for said experiments. We then show that a prompt-based stance control method can reliably guide models to argue for or against a particular topic. Finally, we analyze confounding factors like topic and stance of the initial user prompts. We find that control is easiest when the target stance aligns with topical priors of the model or a user’s prompt. Further, the models maintain a comparable style across target stances — a key prerequisite for pre/post-treatment experiments. Taken together, our results demonstrate that stance-controlled LLMs are feasible and practically useful for experiments on user attitude change.</abstract>
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%0 Conference Proceedings
%T Prompt-Based Stance Control in German: An Evaluation of LLMs for Experimental Research on Attitude Change
%A Omiecienski, Florian
%A Sindermann, Cornelia
%A Falenska, Agnieszka
%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 omiecienski-etal-2026-prompt
%X How much can Large Language Models (LLMs) influence the attitudes and opinions of their users? Answering this question requires controlled pre/post-treatment experiments, where participants interact with LLMs that consistently adopt a predefined political stance. Such experiments, however, are only possible if LLMs can be reliably steered to hold these stances throughout the interactions. In this work, we evaluate whether state-of-the-art LLMs can be effectively stance-controlled in German, thereby enabling experiments on human–LLM interactions. First, using a corpus of realistic user prompts, we find that LLMs are predominantly neutral, making them infeasible for said experiments. We then show that a prompt-based stance control method can reliably guide models to argue for or against a particular topic. Finally, we analyze confounding factors like topic and stance of the initial user prompts. We find that control is easiest when the target stance aligns with topical priors of the model or a user’s prompt. Further, the models maintain a comparable style across target stances — a key prerequisite for pre/post-treatment experiments. Taken together, our results demonstrate that stance-controlled LLMs are feasible and practically useful for experiments on user attitude change.
%R 10.63317/2tvnax68shcy
%U https://aclanthology.org/2026.lrec-1.644/
%U https://doi.org/10.63317/2tvnax68shcy
%P 8122-8140
Markdown (Informal)
[Prompt-Based Stance Control in German: An Evaluation of LLMs for Experimental Research on Attitude Change](https://aclanthology.org/2026.lrec-1.644/) (Omiecienski et al., LREC 2026)
ACL