Large Language Models (LLMs) have gained significant popularity for their
application in various everyday tasks such as text generation, summarization,
and information retrieval. As the widespread adoption of LLMs continues to
surge, it becomes increasingly crucial to ensure that these models yield
responses that are politically impartial, with the aim of preventing
information bubbles, upholding fairness in representation, and mitigating
confirmation bias. In this paper, we propose a quantitative framework and
pipeline designed to systematically investigate the political orientation of
LLMs. Our investigation delves into the political alignment of LLMs across a
spectrum of eight polarizing topics, spanning from abortion to LGBTQ issues.
Across topics, the results indicate that LLMs exhibit a tendency to provide
responses that closely align with liberal or left-leaning perspectives rather
than conservative or right-leaning ones when user queries include details
pertaining to occupation, race, or political affiliation. The findings
presented in this study not only reaffirm earlier observations regarding the
left-leaning characteristics of LLMs but also surface particular attributes,
such as occupation, that are particularly susceptible to such inclinations even
when directly steered towards conservatism. As a recommendation to avoid these
models providing politicised responses, users should be mindful when crafting
queries, and exercise caution in selecting neutral prompt language.

利用定量框架和流程系统地调查大型语言模型的政治取向，研究结果显示在八个极化话题中，当用户查询涉及职业、种族或政治取向时，大型语言模型倾向于提供与自由派或左倾观点更为接近的回答，而不是保守派或右倾观点。为了避免这些模型提供政治化的回答，用户在构建查询时应谨慎，并选择中立的提示语言。