This paper investigates the voting behaviors of Large Language Models (LLMs),
particularly OpenAI's GPT4 and LLaMA2, and their alignment with human voting
patterns. Our approach included a human voting experiment to establish a
baseline for human preferences and a parallel experiment with LLM agents. The
study focused on both collective outcomes and individual preferences, revealing
differences in decision-making and inherent biases between humans and LLMs. We
observed a trade-off between preference diversity and alignment in LLMs, with a
tendency towards more uniform choices as compared to the diverse preferences of
human voters. This finding indicates that LLMs could lead to more homogenized
collective outcomes when used in voting assistance, underscoring the need for
cautious integration of LLMs into democratic processes.

通过调查大型语言模型（LLMs），特别是 OpenAI 的 GPT4 和 LLaMA2 的选举行为及其与人类选举模式的一致性，本研究通过人类投票实验和与 LLM 代理人的平行实验，着重研究了集体结果和个人偏好，揭示了人类和 LLMs 在决策和固有偏见方面的差异。我们发现 LLMs 在偏好多样性和一致性之间存在一种权衡，倾向于相对人类选民多样化的选择，这表明在投票辅助中使用 LLMs 可能导致更趋同的集体结果，强调了谨慎将 LLMs 整合到民主进程中的必要性。

LLM 投票：人类选择与 AI 集体决策

LLM Voting: Human Choices and AI Collective Decision Making

Traditional media outlets are known to report political news in a biased way,
potentially affecting the political beliefs of the audience and even altering
their voting behaviors. Many researchers focus on automatically detecting and
identifying media bias in the news, but only very few studies exist that
systematically analyze how theses biases can be best visualized and
communicated. We create three manually annotated datasets and test varying
visualization strategies. The results show no strong effects of becoming aware
of the bias of the treatment groups compared to the control group, although a
visualization of hand-annotated bias communicated bias instances more
effectively than a framing visualization. Showing participants an overview
page, which opposes different viewpoints on the same topic, does not yield
differences in respondents' bias perception. Using a multilevel model, we find
that perceived journalist bias is significantly related to perceived political
extremeness and impartiality of the article.

研究表明，媒体的政治偏见会影响受众的政治信仰及投票行为。通过三个手动注释的数据集及不同的可视化策略测试，结果表明手动注释偏见的可视化比框架可视化更有效。通过多层次模型，可以发现记者的偏见与文章的政治极端性和公正性有显著联系。