通过道德基础理论研究大型语言模型中的政治与人口统计学关联
Investigating Political and Demographic Associations in Large Language Models Through Moral Foundations Theory
AI总结：
本研究直接对比大型语言模型与人类的道德基础理论回应，通过显式提示和人口统计学角色扮演，系统评估LLM在政治与道德领域中的意识形态倾向及其表征准确性。
摘要：
大型语言模型（LLMs）已日益融入许多互联网用户的日常生活，在医学、人际关系甚至法律事务等领域扮演着重要的建议提供者角色。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如何做出回应以及回应内容的问题，尤其是关于潜在偏见的问题。为了量化LLMs中潜在偏见的性质，多项研究应用了道德基础理论（MFT），该框架将人类道德推理分为五个维度：伤害、公平、内群体忠诚、权威和纯洁。先前的研究已使用MFT来衡量人类参与者在政治、国家和文化维度上的差异。虽然已有一些研究分析了LLMs在角色扮演场景中对政治立场的回应，但迄今为止，尚无工作直接评估LLM回应中的道德倾向，也未将LLM输出与可靠的人类数据联系起来。在本文中，我们直接分析LLM的MFT回应与现有人类研究之间的差异，调查普遍可用的LLM回应是否表现出意识形态倾向：无论是通过其固有回应、对政治意识形态的直接表征，还是当从构建的人类角色视角进行回应时。我们评估LLMs是否固有地生成更贴近某一种政治意识形态而非另一种的回应，并进一步通过显式提示和基于人口统计学的角色扮演来检验LLMs表征意识形态视角的准确性。通过在这些条件和实验中系统地分析LLM行为，我们的研究为AI生成回应中政治和人口统计学依赖的程度提供了见解。
英文摘要：
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.
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