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.

翻译：本文研究了大语言模型（LLMs）的投票行为，特别是OpenAI的GPT4和LLaMA2，及其与人类投票模式的契合程度。我们的研究方法包括一项人类投票实验以建立人类偏好的基准，以及一项与LLM智能体并行的实验。研究聚焦于集体结果和个体偏好两方面，揭示了人类与LLM在决策过程和固有偏见上的差异。我们观察到LLM在偏好多样性与对齐之间存在权衡，与人类投票者多样的偏好相比，LLM倾向于做出更趋同的选择。这一发现表明，在投票辅助中使用LLM可能导致更同质化的集体结果，凸显了将LLM审慎整合到民主进程中的必要性。