与大型语言模型的对话减少了阴谋论的信念,即使当人工智能被认为是人类时
Esther Boissin1, Thomas H Costello2, Daniel Spinoza-Martín1
1Department of Psychology, Cornell University, Ithaca, NY 14853, USA.
PNAS nexus
|November 3, 2025
概括
大型语言模型 (LLM) 通过基于事实的对话有效地减少阴谋论. 人工智能使者的人工智能使者.
科学领域:
- 心理学 心理学 心理学
- 人工智能的人工智能
- 沟通研究 沟通研究
背景情况:
- 阴谋论的信念往往很难被纠正.
- 最近的研究表明,大型语言模型 (LLM) 可以通过个性化对话来减少这些信念.
- 人工智能说服在纠正信念方面的有效性尚未完全被理解,特别是关于信使的身份.
研究的目的:
- 调查人工智能驱动的信念纠正的有效性是否依赖于人工智能信使或有说服力的内容.
- 为了比较人工智能的影响与人类信使提供基于事实的反辩.
- 检查对话语调在AI说服中的作用.
主要方法:
- 对955名参与者进行了一项预先注册的实验.
- 参与者报告说,要么有阴谋论的信念,要么是认识论上不合理的信念.
- 参与者与一个反对他们的信念的LLM互动,条件不同,LLM的特征 (AI与人类专家) 和对话语调不同.
主要成果:
- 由LLM驱动的对话显著降低了参与者对阴谋论信仰和认识论上不合理的信仰的信心.
- 不同的信使条件 (人工智能与人类) 或对话语调之间没有观察到信念减少的显著差异.
- 该LLM的说服力的有效性是独立于它是否被视为人工智能或人类专家.
结论:
- 在纠正不合理的信念方面,人工智能说服是有效的,因为生成的信息具有令人信服的性质,而不是使者的AI身份.
- 通过人工智能提供的基于事实的论证与人类专家提供的论证同样有效.
- 未来的研究应该专注于优化信息内容,以纠正信念,无论信使的性质如何.
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