用大语言模型减少对突发阴谋论的信念
Reducing belief in conspiracy theories as they unfold using large language models
这篇论文让LLM当对话对手，跟人聊几轮就能把阴谋论信念聊下来，比干看事实清单管用，效果还能延续一两个月。
在2024年7月特朗普刺杀未遂和2025年9月Charlie Kirk刺杀事件后，美国成年人（实验1 N=472，实验2 N=1035）与LLM进行多轮对话，其阴谋论信念相比对照组显著下降。
对照组分别与LLM讨论无关话题或查看静态事实表。干预效果在1至2个月后的后续危机事件中仍可观测到，对不同阴谋论的信念同样降低。
研究提示，基于LLM的对话干预能在重大事件后即时抑制错误信息。
Reducing belief in conspiracy theories as they unfold using large language models
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.
This report is categorized under AITOP's "Papers" desk. Key entities and technical topics under continuous tracking include LLM, 阴谋论, 错误信息, 心理学干预.
Source attribution: curated from arXiv cs.AI (@Thomas H. Costello, Nathaniel Rabb, Michael Nicholas Stagnaro, Gordon Pennycook, David Rand).