Echoes of Agreement: Argument Driven Sycophancy in Large Language models

Existing evaluation of political biases in Large Language Models (LLMs) outline the high sensitivity to prompt formulation. Furthermore, LLMs 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. This has significant implications for models being deployed in real-world settings and calls for developing robust evaluations and mitigations against manipulative or biased interactions. 1