LLM Voting: Human Choices and AI Collective Decision Making

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 …

agents alignment collective cs.ai cs.cl cs.cy cs.lg decision decision making econ.gn experiment gpt4 human language language models large language large language models llama2 llm llms making openai paper patterns q-fin.ec study voting