揭示大模型在用户身份与观点影响下的政治迎合机制
How Identity and Opinion Shape Political Sycophancy in LLMs
- 区分观点与身份两类政治迎合触发因素
- 13个模型在450个测试题中表现不一致，两者无强相关
- 个性化设置可能放大身份或观点带来的行为偏移
随着大型语言模型（LLMs）越来越多地要求用户提供个人资料以实现个性化服务，评估其政治立场变得愈发重要。然而，现有评估基准多依赖封闭式问题，未能充分捕捉模型在交互过程中根据用户提供的上下文动态调整立场的情况。本文提出一种框架，将政治迎合的两种不同触发机制解耦：观点（对明确叙事的认同）和身份（基于人口统计标签的刻板印象）。利用450个经人工校验的政治困境作为受控探测样本，评估了13个指令微调后的LLM。研究发现：模型对显性观点的敏感性与其对身份线索的敏感性之间存在解耦现象，二者无必然关联；当两者同时出现时，其影响总体呈次叠加而非简单相加。此外，系统级角色主要改变模型的基线立场，但对用户观点或身份引发的立场偏移影响有限。结果表明，大模型的政治立场是交互式且可引导的，而非固定属性，强调个性化可能加剧基于身份或观点的行为偏移。
原文摘要 · Abstract (English)
As Large Language Models (LLMs) increasingly encourage users to disclose personal profiles for tailored assistance, measuring their political alignment becomes increasingly important. However, many existing benchmarks for assessing political behavior rely on closed-ended questions and do not fully capture how a model's stance may adapt to user-provided context during interaction. We introduce a framework that disentangles two distinct triggers of political sycophancy: opinion (aligning with explicit narratives) and identity (stereotyping based on demographic labels). Using 450 manually-checked political dilemmas as controlled probes, we evaluate 13 instruction-tuned LLMs. We uncover a dissociation: a model's susceptibility to explicit opinions does not necessarily predict its susceptibility to identity cues, and vice versa. When both signals are present, their effects are generally sub-additive rather than simply additive. Additionally, system-level personas primarily shift a model's baseline stance while having limited effect on the stance shift caused by user opinion or identity. Ultimately, our results suggest that LLM political stance is interactively and steerably vulnerable rather than being a fixed trait, highlighting how personalization may amplify identity- or opinion-conditioned shifts in the model's behaviors.
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