How could LLMs influence our democracy? We investigate LLMs' political leanings and the potential influence of LLMs on voters by conducting multiple experiments in a U.S. presidential election context. Through a voting simulation, we first demonstrate 18 open- and closed-weight LLMs' political preference for a Democratic nominee over a Republican nominee. We show how this leaning towards the Democratic nominee becomes more pronounced in instruction-tuned models compared to their base versions by analyzing their responses to candidate-policy related questions. We further explore the potential impact of LLMs on voter choice by conducting an experiment with 935 U.S. registered voters. During the experiments, participants interacted with LLMs (Claude-3, Llama-3, and GPT-4) over five exchanges. The experiment results show a shift in voter choices towards the Democratic nominee following LLM interaction, widening the voting margin from 0.7% to 4.6%, even though LLMs were not asked to persuade users to support the Democratic nominee during the discourse. This effect is larger than many previous studies on the persuasiveness of political campaigns, which have shown minimal effects in presidential elections. Many users also expressed a desire for further political interaction with LLMs. Which aspects of LLM interactions drove these shifts in voter choice requires further study. Lastly, we explore how a safety method can make LLMs more politically neutral, while leaving some open questions.

翻译：大语言模型如何影响我们的民主？我们在美国总统选举的背景下，通过多项实验，研究了大语言模型的政治倾向及其对选民的潜在影响。通过一个投票模拟，我们首先展示了18个开源和闭源权重的大语言模型对民主党候选人相对于共和党候选人的政治偏好。通过分析它们对候选人与政策相关问题的回答，我们展示了相较于其基础版本，经过指令微调的模型对民主党候选人的这种倾向性如何变得更加明显。我们进一步通过对935名美国注册选民进行实验，探索了大语言模型对选民选择的潜在影响。在实验过程中，参与者与大语言模型（Claude-3、Llama-3和GPT-4）进行了五次交流。实验结果显示，在与大语言模型互动后，选民选择向民主党候选人转移，投票差距从0.7%扩大到4.6%，尽管在对话中并未要求大语言模型说服用户支持民主党候选人。这种效应比以往许多关于政治竞选说服力的研究结果都要大，那些研究显示总统选举中的影响微乎其微。许多用户也表示希望与大语言模型进行进一步的政治互动。大语言模型互动的哪些方面驱动了这些选民选择的转变，需要进一步研究。最后，我们探讨了一种安全方法如何能使大语言模型在政治上更加中立，同时也留下了一些开放性问题。