在前沿大语言模型中评估政治说服风险基准
Benchmarking Political Persuasion Risks Across Frontier Large Language Models
AI总结：
本研究评估了前沿大语言模型在政治说服方面的风险，发现Claude模型说服力最强，Grok最弱，且信息提示效果因模型而异。
摘要：
人们对大型语言模型（LLMs）影响政治观点能力的担忧依然存在。尽管先前研究声称LLMs的说服力并不强于传统政治竞选活动，但前沿模型的兴起需要进一步研究。在两项调查实验（N=19,145）中，我们评估了由Anthropic、OpenAI、Google和xAI开发的七种最先进的LLMs。我们发现LLMs在传统竞选广告上表现更优，且模型间表现存在差异。具体而言，Claude模型的说服力最高，而Grok最低。结果在不同议题和立场上均稳健。此外，与Hackenburg等人（2025b）和Lin等人（2025）发现信息提示增强说服力不同，我们发现信息提示的有效性取决于模型：它们增强了Claude和Grok的说服力，但显著降低了GPT的说服力。我们引入了一种数据驱动且策略无关的LLM辅助对话分析方法，以识别和评估潜在的说服策略。我们的工作评估了前沿模型的说服风险，并提供了一种跨模型的风险评估框架。
英文摘要：
Concerns persist regarding the capacity of Large Language Models (LLMs) to sway political views. Although prior research has claimed that LLMs are not more persuasive than standard political campaign practices, the recent rise of frontier models warrants further study. In two survey experiments (N=19,145) across bipartisan issues and stances, we evaluate seven state-of-the-art LLMs developed by Anthropic, OpenAI, Google, and xAI. We find that LLMs outperform standard campaign advertisements, with heterogeneity in performance across models. Specifically, Claude models exhibit the highest persuasiveness, while Grok exhibits the lowest. The results are robust across issues and stances. Moreover, in contrast to the findings in Hackenburg et al. (2025b) and Lin et al. (2025) that information-based prompts boost persuasiveness, we find that the effectiveness of information-based prompts is model-dependent: they increase the persuasiveness of Claude and Grok while substantially reducing that of GPT. We introduce a data-driven and strategy-agnostic LLM-assisted conversation analysis approach to identify and assess underlying persuasive strategies. Our work benchmarks the persuasive risks of frontier models and provides a framework for cross-model comparative risk assessment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
