LLM Voting: Human Choices and AI Collective Decision Making
Joshua C. Yang Marcin Korecki Damian Dailisan Carina I. Hausladen Dirk Helbing
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.