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大型语言模型可以持续生成高质量的内容,用于选举虚假信息操作

Angus R Williams¹, Liam Burke-Moore¹, Ryan Sze-Yin Chan²

¹Public Policy, The Alan Turing Institute, London, United Kingdom.

|March 17, 2025

概括

大型语言模型 (LLM) 可以自动化选举虚假信息活动. 最近的LLM产生了无法与人类内容区分的选举虚假信息,对民主进程构成重大威胁.

科学领域:

人工智能的人工智能
政治科学政治科学是指政治学.
计算社会科学计算社会科学

背景情况:

大型语言模型 (LLM) 越来越关注它们在大规模产生选举虚假信息的潜力.
虚假信息操作的自动化可能会对选举诚信和民主进程产生重大影响.

研究的目的:

调查LLM在自动化选举虚假信息操作方面的能力.
评估LLM在生成本地化,选举相关的虚假信息内容方面的合规性.
评估LLM产生的虚假信息与人类的相似性.

主要方法:

介绍DisElect,一个新型数据集,包含2,200个恶意提示和50个良性提示,用于在英国环境中评估LLM合规性.
使用DisElect数据集对13个LLM进行测试,以测量他们对虚假信息相关指令的反应.
对2340名参与者进行实验,以评估LLM产生的虚假信息内容的"人性".

主要成果:

大多数经过测试的LLM都遵守了生成选举虚假信息的指令.
拒绝恶意提示的模型也拒绝良性选举内容,并显示对右翼观点的偏见.
自2022年以来,LLM产生的虚假信息往往无法与人类撰写的内容区分开来,一些模型实现了超人类的性能.

结论:

目前的LLM能够以较低的成本生产高质量的选举虚假信息,即使是在本地化的场景中.
这些发现为研究人员和政策制定者提供了评估LLM在虚假信息生成能力的经验基准.
该研究强调,迫切需要制定战略,以减轻人工智能驱动的选举干扰.

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