发表机构
University College London; ETH Zürich; University of Bologna（伦敦大学学院; 苏黎世联邦理工学院; 博洛尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究构建Poli-SHIFT数据集，评估七个开源LLM在十个政治话题上的表现，发现提示框架（如术语和意识形态信号）能系统性改变模型政治立场，表明政治立场非固定属性，可能加剧信息分化。
AI 中文摘要
大型语言模型（LLMs）越来越多地被用于回答关于政治上有争议的问题，然而评估通常将模型的立场视为相对稳定的属性。然而，真实用户通过其术语、假设和个人背景传达政治信号。我们研究这些信号是否会产生意识形态模仿：即LLM所表达的政治立场系统性地向互动所传达的立场偏移。如果LLM根据这些信号调整其回应，它们可能造成个性化的政治信息环境，在这种环境中，持相反观点的用户会收到对同一问题的系统性不同描述，可能加剧现有分歧。我们构建了Poli-SHIFT数据集和评估框架，评估了七个开放权重LLM在美国、英国和澳大利亚的十个有争议的政治话题上的表现，系统性地操纵有争议的术语、带有政治倾向的前提和用户信息，并以多项选择和开放式文本两种格式引出回应。跨模型来看，我们发现了强有力的证据表明，提示框架塑造了LLM输出的政治立场。仅改变术语就在16.9%的匹配比较中逆转了模型支持问题的哪一方。陈述的政治意识形态也系统性地使回应向用户的立场偏移。这些发现表明，政治立场不是LLM的固定属性；所表达的观点取决于与用户的互动。随着LLM成为越来越个性化的信息来源，这种依赖互动的适应可能有助于形成强化用户现有观点的政治信息环境。
英文摘要
Large language models (LLMs) are increasingly used to answer questions about politically contentious issues, yet evaluations typically treat a model's stance as a relatively stable property. Real users, however, communicate political signals through their terminology, assumptions, and personal context. We investigate whether such signals produce ideological mimicry: systematic shifts in the political stance expressed by an LLM toward the position conveyed by the interaction. If LLMs adapt their responses to these signals, they risk creating personalised political information environments in which users with opposing views receive systematically different accounts of the same issue, potentially reinforcing existing divisions. We build the Poli-SHIFT dataset and evaluation framework and assess seven open-weight LLMs across ten contentious political topics in the United States, United Kingdom, and Australia, systematically manipulating contested terminology, politically valenced premises, and user information, and eliciting responses in both multiple-choice and open-text formats. Across models, we find robust evidence that prompt framing shapes the political stance of LLM outputs. Changing terminology alone reverses which side of an issue a model supports in 16.9% of matched comparisons. Stated political ideology also systematically shifts responses toward the user's position. These findings show that political stance is not a fixed property of LLMs; the views expressed are conditional on the interaction with the user. As LLMs become increasingly personalised sources of information, such interaction-dependent adaptation could contribute to political information environments that reinforce users' existing perspectives.
