  This paper investigates the voting behaviors of Large Language Models (LLMs),
specifically GPT-4 and LLaMA-2, their biases, and how they align with human
voting patterns. Our methodology involved using a dataset from a human voting
experiment to establish a baseline for human preferences and a corresponding
experiment with LLM agents. We observed that the methods used for voting input
and the presentation of choices influence LLM voting behavior. We discovered
that varying the persona can reduce some of these biases and enhance alignment
with human choices. While the Chain-of-Thought approach did not improve
prediction accuracy, it has potential for AI explainability in the voting
process. We also identified a trade-off between preference diversity and
alignment accuracy in LLMs, influenced by different temperature settings. Our
findings indicate that LLMs may lead to less diverse collective outcomes and
biased assumptions when used in voting scenarios, emphasizing the importance of
cautious integration of LLMs into democratic processes.
