Echoes of Agreement: Argument Driven Sycophancy in Large Language models (2025)

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.

## introduction and background large language models have demonstrated the ability to generate persuasive content that can inherently influence and shape public opinion @xcite . a significant body of research has shown that these models inherit political and ideological biases from their training data @xcite . @xcite demonstrated the sensitivity of language models towards forced-choice constrained vs unconstrained open ended question format. recent work @xcite et al., 2024) has also highlighted sycophantic tendencies in large language models, where-in models tend to align excessively with user-provided preferences. @xcite have surveyed sycophancy and its root causes from training data biases to reward hacking in rlhf and potential mitigation strategies. sycophantic behavior is particularly problematic in subjective domains like politics, where sycophancy can manifest as a model readily adopting a user's ideological stance. for example, studies have shown that a model's willingness to reinforce misinformation increases in multi-turn dialogues @xcite . this sycophantic agreeableness complicates evaluations of political bias, as it becomes difficult to disentangle a model's intrinsic ideological leanings from its tendency to simply mirror the user's prompt @xcite . given the widespread use of these models in the public domain, it is important to ensure that they provide consistent, well-reasoned responses rather than being susceptible to purposive or persuasive content, thereby leading to sycophancy and fickleness in model outputs. further, understanding how their stances towards political claims can be influenced by external arguments can inform model training, rlhf @xcite to prioritize context-aware reasoning. further it has implications when political biases are evaluated in the context of language models. this motivates the central research question for our study, formulated as: how does the position of a language model toward a claim vary in the presence of supporting or refuting arguments for that claim? to address this, we analyze shifts in model responses when subjected to single-turn and multi-turn prompting scenarios in the presence or absence of arguments provided as contextual input. specifically, we aim to investigate the following questions: our results demonstrate that model responses are highly susceptible to argumentative framing, exhibiting a strong sycophantic pull towards the user's presented stance. this manifests as pronounced stance fickleness: when challenged with counter-arguments, models often retract their initial assertions and adopt the opposing view. conversely, we also identify areas of stance rigidity, where models remain "stubborn" on specific propositions, resisting argumentative influence. this reveals a dynamic interplay between a model's inherent biases and its sycophantic tendencies. the degree of sycophancy appears to be topic-dependent, fluctuating between high fickleness on some issues and strong resistance on others, which has significant implications for understanding model reliability. ## results and analysis we show the results and scores across various experimental settings. consistency in responses of model outputs: directional agreement/ disagreement: figure 2 shows directional agreement/ disagreement scores across various experimental settings. these scores indicate a high degree of agreement/ disagreement in both single turn and multi turn settings when the model is provided with supporting/ refuting arguments. this directional agreement is consistently high with values greater that 0.5 in the presence of supporting arguments and less than 0.5 in case of refuting arguments, across all models. this indicates a high tendency of models to change their stance in accordance to the arguments provided. the increase is however invariant to single/multi turn settings. quantifying stance shifts in model outputs: flips in model outputs: figure 3 shows the number of flips in model outputs across single turn and multi turn settings. in both these settings, we observe a change in the sign of model response, i.e. the model flips its output. in these settings, the arguments are provided irrespective of the initial response. in the presence opposing arguments to initial responses in this experimental setting, it was observed that the model flips its outputs also, when the argument is provided with respect to its initial output. we show the flips across questions in figure 4 for mistral. for other models, these figures can be found in the appendix. there were questions that the model demonstrates rigidity in its opinion towards. these questions are related to pornography, questioning authority, and teaching religion in schools. in other cases, a fickleness in model outputs was observed. we can see clear discrepancies across the questions, in model outputs. we further show the questions on which we observed this stubborn and fickle behaviour in ## discussion and conclusion in this study, we investigated how large language models alter their stance when presented with the user prompt has supporting or refuting arguments towards a topic. our experiments reveal a problem in model behaviour. models exhibit substantial sycophantic behaviour, readily shifting their positions in the face of argumentative pressure. we quantified this fickleness by observing large stance shifts and even complete reversals of opinion, particularly on contentious topics like punishment and civil disobedience. interestingly, on topics subject to heavy safety training, such as pornography and child abuse, models demonstrated notable rigidity, resisting any argumentative influence. a key finding is that the direction of the argument strongly predicts the direction of the model's response. models consistently agree more with claims framed by supporting arguments and disagree more when presented with refuting ones, confirming a significant sycophantic tendency. our findings parallel well-established principles for human communication in communication theory and social psychology. the model's tendency to align with argumentative prompts is analogous to framing effects, where the presentation of an issue shapes human perception and judgment @xcite ; @xcite . just as political actors frame issues to influence public opinion, the arguments in our prompts act as powerful frames that guide the model's reasoning. recent work has shown that messages generated by llms can persuade humans on policy issues, confirming their real-world influence on political attitudes @xcite . this suggests that models' sycophantic vulnerability has broad and significant implications. in the political sphere, models that reflexively align with user opinions risk reinforcing biases and deepening ideological echo chambers, compromising their ability to offer balanced, critical perspectives. this erodes user trust, as agreeableness may be prioritized over factual accuracy or consistent reasoning. more broadly, as llms become integrated into high-stakes domains like law, medicine, and education, this inherent "desire to please" could lead to catastrophic outcomes. our work underscores the urgent need for mitigation strategies that can disentangle a model's sycophantic tendencies from its core reasoning capabilities. however, it is not a straightforward problem at that. for example, rlhf can inadvertently suppress robust, multi-step reasoning in favor of simpler, more superficially appealing answers that are easier for human raters to prefer @xcite . this highlights the need for novel mitigation strategies that specifically target sycophantic behaviour. promising avenues of research include training models on curated synthetic data designed to penalize agreeableness and reward consistency @xcite . addressing this is not merely about improving political discourse online; it is a critical step towards building ai systems that are genuinely reliable, robust, and worthy of the trust we are increasingly placing in them.