评估大型语言模型聊天机器人对民主社会构成的说服风险的框架
A Framework to Assess the Persuasion Risks Large Language Model Chatbots Pose to Democratic Societies
AI总结：
本研究通过两项调查实验和真实世界模拟评估了LLM聊天机器人在政治说服中的成本效益与风险，发现其单次说服力与竞选广告相当且成本更低，但目前规模化扩展难度较大。
摘要：
近年来，大型语言模型（LLM）通过其说服能力对民主社会构成的潜在威胁引发了严重关切。我们在现有研究的基础上进行了两项调查实验和一项真实世界模拟演练，以确定与标准政治竞选实践相比，使用LLM聊天机器人说服大量选民是否更具成本效益，同时考虑了说服过程中的“接收”和“接受”步骤（Zaller 1992）。这些实验通过评估人类与LLM之间的扩展交互（而非单次交互），以及评估短期和长期的说服效果（而非仅仅要求用户对LLM生成内容的说服力进行评分），改进了以往的工作。在跨越三个不同政治领域的两项调查实验（N = 10,417）中，我们发现，一旦选民接触到LLM，其说服力与实际的竞选广告大致相当，但现实世界中的政治说服既取决于对说服性信息的接触，也取决于接触条件下的影响。通过基于现实世界参数的模拟，我们估计，当计入接触成本时，基于LLM的说服成本在每位被说服选民48至74美元之间，而传统竞选方法为100美元。然而，目前扩大传统竞选说服方法的规模要比扩大基于LLM的说服容易得多。虽然目前LLM似乎并不比现有的非LLM方法具有更大规模政治说服的潜力，但随着LLM能力的不断提高以及可扩展地鼓励接触说服性LLM变得更加容易，这种情况可能会发生改变。
英文摘要：
In recent years, significant concern has emerged regarding the potential threat that Large Language Models (LLMs) pose to democratic societies through their persuasive capabilities. We expand upon existing research by conducting two survey experiments and a real-world simulation exercise to determine whether it is more cost effective to persuade a large number of voters using LLM chatbots compared to standard political campaign practice, taking into account both the "receive" and "accept" steps in the persuasion process (Zaller 1992). These experiments improve upon previous work by assessing extended interactions between humans and LLMs (instead of using single-shot interactions) and by assessing both short- and long-run persuasive effects (rather than simply asking users to rate the persuasiveness of LLM-produced content). In two survey experiments (N = 10,417) across three distinct political domains, we find that while LLMs are about as persuasive as actual campaign ads once voters are exposed to them, political persuasion in the real-world depends on both exposure to a persuasive message and its impact conditional on exposure. Through simulations based on real-world parameters, we estimate that LLM-based persuasion costs between \$48-\$74 per persuaded voter compared to \$100 for traditional campaign methods, when accounting for the costs of exposure. However, it is currently much easier to scale traditional campaign persuasion methods than LLM-based persuasion. While LLMs do not currently appear to have substantially greater potential for large-scale political persuasion than existing non-LLM methods, this may change as LLM capabilities continue to improve and it becomes easier to scalably encourage exposure to persuasive LLMs.
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。
