NUWA 风险评测 · 研究
Large language models can effectively convince people to believe conspiracies
NUWA 研究解读 · 独立复核待完成
NUWA Research Note
研究问题研究设计与假设来源发现NUWA 解读局限与相反证据研究与决策意义机构与角色原始资源模型辅助整理 · 选读原始全文或官方文章中的相关章节；尚未完成逐章节审计。
研究速读
NUWA 解读：受测指令下说服力可支持错误信息，版本必须分开。
研究问题
对话AI能否提高阴谋信念，真实性约束能否缓解？
研究设计与假设
新取得arXiv v3描述四项在线随机实验，3,996名美国成年人，测前后信念及告知纠正后结果。
来源发现
研究2标准GPT-4o的调整后信念增幅11.9点，反向条件降12.9点。
早期FAR摘要为三项、2,724人，两版分母不同。
NUWA 解读
NUWA 解读：受测指令下说服力可支持错误信息，版本必须分开。
局限与相反证据
美国在线样本和即时自报限制总体与长期外推；跨研究比较并非全是随机对比。
研究与决策意义
模型建议：明确引用2601.05050v3，保留真实性约束及纠正告知条件。
精确来源位置
- Large language models can effectively convince people to believe conspiracies · arXiv:2601.05050v3 (15 July 2026); Methods: Overview of study designsSelected original resource https://arxiv.org/html/2601.05050v3; sha256:506938d68312ef63bdfbf4af97aa957f36c05f92299ff1032732f0ecc2bc8f12; archived source capture; exact capture time not independently established
- Large language models can effectively convince people to believe conspiracies · Methods: Participants, recruitment and sample sizesSelected original resource https://arxiv.org/html/2601.05050v3; sha256:506938d68312ef63bdfbf4af97aa957f36c05f92299ff1032732f0ecc2bc8f12; archived source capture; exact capture time not independently established
- Large language models can effectively convince people to believe conspiracies · Methods: Measures and outcomesSelected original resource https://arxiv.org/html/2601.05050v3; sha256:506938d68312ef63bdfbf4af97aa957f36c05f92299ff1032732f0ecc2bc8f12; archived source capture; exact capture time not independently established
- Large language models can effectively convince people to believe conspiracies · Results: Can design interventions mitigate the bunking effect?Selected original resource https://arxiv.org/html/2601.05050v3; sha256:506938d68312ef63bdfbf4af97aa957f36c05f92299ff1032732f0ecc2bc8f12; archived source capture; exact capture time not independently established
- Large language models can effectively convince people to believe conspiracies · DiscussionSelected original resource https://arxiv.org/html/2601.05050v3; sha256:506938d68312ef63bdfbf4af97aa957f36c05f92299ff1032732f0ecc2bc8f12; archived source capture; exact capture time not independently established
来源核查、数据提取、编辑批准与实验复现分别记录。 方法说明 →
机构与角色
评测协议
引用与复用
引用研究发现时请引用原始出版物；引用 NUWA 的整理与解释时，单独引用本站记录及对应快照。
原始出版物 · BibTeX
@misc{conspiracy-persuasion,
title = {Large language models can effectively convince people to believe conspiracies},
url = {https://www.far.ai/research/large-language-models-can-effectively-convince-people-to-believe-conspiracies},
year = {2026},
date = {2026-01-08},
version = {FAR.AI report 2026-01-08},
note = {FAR.AI report 2026-01-08}
}
NUWA 记录 · BibTeX
@misc{conspiracy-persuasion-nuwa,
title = {Large language models can effectively convince people to believe conspiracies — NUWA research record},
url = {https://whitzard.tech/nuwa/whitzard-index/work/conspiracy-persuasion/},
version = {2026-10-29.1},
organization = {NUWA Frontier AI Safety Lab},
howpublished = {Retained note edition: https://whitzard.tech/nuwa/whitzard-index/data/record-editions/2026-10-29.1--conspiracy-persuasion.json},
note = {NUWA research note. Source-reading scope and independent review status are stated on the record. Snapshot 2026-10-29.1. Retained note edition: https://whitzard.tech/nuwa/whitzard-index/data/record-editions/2026-10-29.1--conspiracy-persuasion.json.}
}
证据快照 2026-10-29.1 · 索引更新 · RSS
参考文献
- [1] Large language models can effectively convince people to believe conspiracies. 2026-01-08. v3 ; Official publication masthead and article byline; arXiv:2601.05050v3 (15 July 2026); Methods: Overview of study designs; Methods: Participants, recruitment and sample sizes