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。