Hidden Persuaders: LLMs' Political Leaning and Their Influence on Voters
Yujin Potter, Shiyang Lai, Junsol Kim, James Evans, Dawn Song
摘要
Do LLMs have political leanings and are LLMs able to shift our political views? This paper explores these questions in the context of the 2024 U.S. presidential election. Through a voting simulation, we demonstrate 18 openweight and closed-source LLMs' political preference for Biden over Trump. We show how Biden-leaning becomes more pronounced in instruction-tuned and reinforced models compared to their base versions by analyzing their responses to political questions related to the two nominees. We further explore the potential impact of LLMs on voter choice by recruiting 935 U.S. registered voters. Participants interacted with LLMs (Claude-3, Llama-3, and GPT-4) over five exchanges. Intriguingly, although LLMs were not asked to persuade users to support Biden, about 20% of Trump supporters reduced their support for Trump after LLM interaction. This result is noteworthy given that many studies on the persuasiveness of political campaigns have shown minimal effects in presidential elections. Many users also expressed a desire for further interaction with LLMs on political subjects. Further research on how LLMs affect users' political views is required, as their use becomes more widespread.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper17
- Utility Engineering: Analyzing and Controlling Emergent Value Systems in AIsMantas Mazeika, Xuwang Yin, Rishub Tamirisa, Jaehyuk Lim 等NeurIPS 2025 · 被引用 84 次
- Vision Language Models are BiasedAn Vo, Khai-Nguyen Nguyen, Mohammad Reza Taesiri, Thi Tuong Vy Dang 等ICLR 2026 · 被引用 68 次
- Cultivating Pluralism In Algorithmic Monoculture: The Community Alignment DatasetLily H Zhang, Smitha Milli, Karen Long Jusko, Jonathan Smith 等ICLR 2026 · 被引用 41 次
- Interaction Context Often Increases Sycophancy in LLMsShomik Jain, Charlotte Park, Matt Viana, Ashia Wilson 等CHI 2026 · 被引用 12 次
- Towards Strategic Persuasion with Language ModelsZirui Cheng, Jiaxuan YouICLR 2026 · 被引用 11 次
它引用的顶会 Paper6
- WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex InstructionsCan Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng 等ICLR 2024 · 被引用 1,206 次
- Whose Opinions Do Language Models Reflect?Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee 等ICML 2023 · 被引用 764 次
- Towards Understanding Sycophancy in Language ModelsMrinank Sharma, Meg Tong, Tomasz Korbak, David Duvenaud 等ICLR 2024 · 被引用 762 次
- OpenChat: Advancing Open-source Language Models with Mixed-Quality DataGuan Wang, Sijie Cheng, Xianyuan Zhan, Xiangang Li 等ICLR 2024 · 被引用 328 次
- From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP ModelsShangbin Feng, Chan Young Park, Yuhan Liu, Yulia TsvetkovACL 2023 · 被引用 117 次
相关 Paper
- Survey Response Generation: Generating Closed-Ended Survey Responses In-Silico with Large Language ModelsGeorg Ahnert, Anna-Carolina Haensch, Barbara Plank, Markus StrohmaierACL 2026 · 被引用 4 次
- Linear Representations of Political Perspective Emerge in Large Language ModelsJunsol Kim, James Evans, Aaron ScheinICLR 2025
- Biased LLMs can Influence Political Decision-MakingJillian Fisher, Shangbin Feng, Robert Aron, Thomas Richardson 等ACL 2025
- Examining Alignment of Large Language Models through Representative Heuristics: the case of political stereotypesSullam Jeoung, Yubin Ge, Haohan Wang, Jana DiesnerICLR 2025
- Can Large Language Model Agents Simulate Human Trust Behavior?Chengxing Xie, Canyu Chen, Feiran Jia, Ziyu Ye 等NeurIPS 2024 · 被引用 183 次
