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.

本研究探讨了大语言模型（LLMs）的政治倾向以及它们对选民的潜在影响，填补了在民主过程中这一领域的研究空白。通过对美国总统选举的多次实验，发现LLMs对民主候选人的倾向性明显，且在与选民的互动中能够显著改变选民的投票选择，投票间距从0.7%扩大至4.6%。这一发现不仅揭示了LLMs在政治领域的影响力，还提出了促进更中立交互方式的必要性。

隐藏的劝说者：大语言模型的政治倾向及其对选民的影响