发表机构
Carnegie Mellon University; Massachusetts Institute of Technology; Cornell University（卡内基梅隆大学; 麻省理工学院; 康奈尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究以特朗普遇刺未遂、查理·柯克遇刺事件后的美国成人为对象，发现经提示降低阴谋论信念的大语言模型（LLM）多轮对话，可显著降低参与者的阴谋论信念，且该处理存在后续影响，凸显了LLM干预对抗错误信息的潜力。
AI 中文摘要
重大事件发生后阴谋论的出现是一项重大社会挑战。本研究测试与大语言模型（LLM）的对话能否降低正在展开的即时阴谋论的可信度。在2024年7月唐纳德·特朗普遇刺未遂事件及2025年9月查理·柯克遇刺事件发生后的数日内开展的实验中，持有针对该危机事件阴谋论观点的美国成年人（实验1：N=472；实验2：N=1035）与经提示可降低其阴谋论信念的LLM进行多轮对话。与对照组参与者（要么与LLM讨论无关话题，要么查看静态事实表）相比，LLM处理组的参与者在两项实验中均表现出显著降低的阴谋论信念。研究还发现LLM处理的后续影响：在后续危机事件发生后的1至2个月内，参与者对不同阴谋论的信念有所降低。这些结果为新兴阴谋论的心理学研究提供了启示，并凸显了以认知为核心的可扩展干预措施在高知名度社会事件发生后即时对抗错误信息的潜力。
英文摘要
The emergence of conspiracy theories in the wake of major events is a significant societal challenge. Here we test whether conversational dialogues with a large language model (LLM) can reduce belief in immediately unfolding conspiracies. In experiments conducted in the days following the July 2024 assassination attempt on Donald Trump and the September 2025 assassination of Charlie Kirk, U.S. adults (Experiment 1: N = 472; Experiment 2: N = 1035) holding conspiratorial views about the crisis event engaged in a multi-turn conversation with an LLM prompted to reduce their conspiracy belief. Compared to control participants who either discussed an irrelevant topic with an LLM or viewed a static fact sheet, participants in the LLM treatment showed significantly reduced conspiracy beliefs in both experiments. We also found evidence of downstream effects of the LLM treatment, observing reduced belief in different conspiracies one to two months later in the wake of subsequent crisis events. These results shed light on the psychology of emerging conspiracies and highlight the potential for scalable, cognitively-focused interventions to counteract misinformation in the immediate aftermath of high-profile societal events.
