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        <title>Echoes of Agreement: Argument Driven Sycophancy in Large Language models</title>
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            <namePart type="family">Christodoulopoulos</namePart>
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        <name type="personal">
            <namePart type="given">Tanmoy</namePart>
            <namePart type="family">Chakraborty</namePart>
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        <name type="personal">
            <namePart type="given">Violet</namePart>
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    <abstract>Existing evaluation of political biases in Large Language Models (LLMs) outline the high sensitivity to prompt formulation. Furthermore, Large Language Models are known to exhibit sycophancy, a tendency to align their outputs with a user’s stated belief, which is often attributed to human feedback during fine-tuning. However, such bias in the presence of explicit argumentation within a prompt remains underexplored. This paper investigates how argumentative prompts induce sycophantic behaviour in LLMs in a political context. Through a series of experiments, we demonstrate that models consistently alter their responses to mirror the stance present expressed by the user. This sycophantic behaviour is observed in both single and multi-turn interactions, and its intensity correlates with argument strength. Our findings establish a link between user stance and model sycophancy, revealing a critical vulnerability that impacts model reliability. Thus has significant implications for models being deployed in real-world settings and calls for developing robust evaluations and mitigations against manipulative or biased interactions.</abstract>
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%0 Conference Proceedings
%T Echoes of Agreement: Argument Driven Sycophancy in Large Language models
%A Kaur, Avneet
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F kaur-2025-echoes
%X Existing evaluation of political biases in Large Language Models (LLMs) outline the high sensitivity to prompt formulation. Furthermore, Large Language Models are known to exhibit sycophancy, a tendency to align their outputs with a user’s stated belief, which is often attributed to human feedback during fine-tuning. However, such bias in the presence of explicit argumentation within a prompt remains underexplored. This paper investigates how argumentative prompts induce sycophantic behaviour in LLMs in a political context. Through a series of experiments, we demonstrate that models consistently alter their responses to mirror the stance present expressed by the user. This sycophantic behaviour is observed in both single and multi-turn interactions, and its intensity correlates with argument strength. Our findings establish a link between user stance and model sycophancy, revealing a critical vulnerability that impacts model reliability. Thus has significant implications for models being deployed in real-world settings and calls for developing robust evaluations and mitigations against manipulative or biased interactions.
%R 10.18653/v1/2025.findings-emnlp.1241
%U https://aclanthology.org/2025.findings-emnlp.1241/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.1241
%P 22803-22812

Markdown (Informal)

[Echoes of Agreement: Argument Driven Sycophancy in Large Language models](https://aclanthology.org/2025.findings-emnlp.1241/) (Kaur, Findings 2025)

• Echoes of Agreement: Argument Driven Sycophancy in Large Language models (Kaur, Findings 2025)

ACL

• Avneet Kaur. 2025. Echoes of Agreement: Argument Driven Sycophancy in Large Language models. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 22803–22812, Suzhou, China. Association for Computational Linguistics.