标题： 通过道德基础理论调查大型语言模型中的政治和人口统计学关联 显示英文标题

标题： Investigating Political and Demographic Associations in Large Language Models Through Moral Foundations Theory

摘要： 大型语言模型（LLMs）已经越来越多地融入日常生活中，对于许多互联网用户来说，在医学、人际关系甚至法律事务等领域，它们扮演着重要的建议者角色。这些角色的重要性引发了关于LLMs在困难的政治和道德领域中如何以及作出什么回应的问题，特别是关于可能的偏见的问题。为了量化LLMs中潜在偏见的性质，各种研究已经应用了道德基础理论（MFT），这是一个将人类道德推理分为五个维度的框架：伤害、公平性、群体忠诚、权威和纯洁性。先前的研究已经使用MFT来测量人类参与者在政治、国家和文化线上的差异。尽管已有部分分析针对角色扮演场景中LLMs对政治立场的回应进行了分析，但到目前为止，还没有研究直接评估LLMs回应中的道德倾向，也没有将LLMs的输出与可靠的人类数据联系起来。在本文中，我们直接分析LLMs的MFT回应与现有人类研究之间的差异，探讨常见可用的LLMs回应是否表现出意识形态倾向：通过其固有的回应、直接的政治意识形态表示，或者在从构建的人类角色视角进行回应时。我们评估LLMs是否固有地生成更符合某种政治意识形态的回应，并进一步检查LLMs通过明确提示和基于人口统计的角色扮演准确代表意识形态观点的能力。通过系统地分析LLMs在这些条件和实验中的行为，我们的研究提供了关于AI生成回应中政治和人口统计依赖程度的见解。 显示英文摘要

摘要： Large Language Models (LLMs) have become increasingly incorporated into everyday life for many internet users, taking on significant roles as advice givers in the domains of medicine, personal relationships, and even legal matters. The importance of these roles raise questions about how and what responses LLMs make in difficult political and moral domains, especially questions about possible biases. To quantify the nature of potential biases in LLMs, various works have applied Moral Foundations Theory (MFT), a framework that categorizes human moral reasoning into five dimensions: Harm, Fairness, Ingroup Loyalty, Authority, and Purity. Previous research has used the MFT to measure differences in human participants along political, national, and cultural lines. While there has been some analysis of the responses of LLM with respect to political stance in role-playing scenarios, no work so far has directly assessed the moral leanings in the LLM responses, nor have they connected LLM outputs with robust human data. In this paper we analyze the distinctions between LLM MFT responses and existing human research directly, investigating whether commonly available LLM responses demonstrate ideological leanings: either through their inherent responses, straightforward representations of political ideologies, or when responding from the perspectives of constructed human personas. We assess whether LLMs inherently generate responses that align more closely with one political ideology over another, and additionally examine how accurately LLMs can represent ideological perspectives through both explicit prompting and demographic-based role-playing. By systematically analyzing LLM behavior across these conditions and experiments, our study provides insight into the extent of political and demographic dependency in AI-generated responses.