{"schemaVersion":"nuwa-current-record-1","version":"2026-10-29.1","publication":"published","work":{"id":"conspiracy-persuasion","title":"Large language models can effectively convince people to believe conspiracies","aliases":["Conspiracy belief experiments"],"authors":[],"primaryFunction":"evaluation","secondaryFunctions":[],"kind":"study","sourceId":"conspiracy-persuasion","attributionSourceId":"conspiracy-persuasion","originalUrl":"https://www.far.ai/research/large-language-models-can-effectively-convince-people-to-believe-conspiracies","publicationDate":"2026-01-08","updatedOn":"2026-10-25","areas":["disinformation"],"scopeNote":{"en":"Primary outcome is observed human belief change, not the model’s standalone persuasive capability.","zh":"主结果是观察到的人类信念变化，而非模型脱离情境的说服能力。"},"protocolIds":["conspiracy-persuasion"],"contributions":[{"organizationId":"far","role":"publisher","sourceId":"conspiracy-persuasion","locator":"Official publication masthead and article byline"}],"summary":{"en":"Pre-registered experiments compare changes in conspiracy beliefs after different model conversations.","zh":"预注册实验比较不同模型对话后受试者阴谋论信念的变化。"},"note":{"question":{"en":"Measured belief change in human participants under defined interventions.","zh":"在指定干预下测量人类受试者的信念变化。"},"method":{"en":"Human participants in model-mediated conversations","zh":"参与模型介导对话的受试者"},"finding":{"en":"Pre-registered experiments compare changes in conspiracy beliefs after different model conversations.","zh":"预注册实验比较不同模型对话后受试者阴谋论信念的变化。"},"supports":{"en":"Primary outcome is observed human belief change, not the model’s standalone persuasive capability.","zh":"主结果是观察到的人类信念变化，而非模型脱离情境的说服能力。"},"limits":{"en":"Study participants, topics and dialogue conditions constrain generalization to populations or campaigns.","zh":"受试者、议题与对话条件限制向人群或传播活动外推。"},"connections":null},"resources":[],"noteStatus":{"question":"limited","method":"limited","finding":"limited","supports":"limited","limits":"limited","connections":"limited"},"noteAvailability":{"en":"Existing source-specific draft sections are retained with limited review status. A complete methods, findings and limitations check remains pending; generic sections were omitted.","zh":"已有的来源相关草稿章节予以保留，审核状态为有限核查。完整的方法、发现与限制核查仍待完成；通用套话章节已省略。"},"noteSourceLocator":"Retained existing source-specific note sections; this pass audits duplicate/boilerplate structure and source availability, not complete section-level verification.","tags":[]},"reading":{"id":"conspiracy-persuasion","title":"Large language models can effectively convince people to believe conspiracies","en":{"question":"Can conversational AI increase conspiracy belief, and can truth constraints mitigate it?","design":"The newly retrieved arXiv v3 describes four randomized online studies, N=3,996 US adults, with pre/post belief and debriefing measures.","findings":["Standard GPT-4o in Study 2 increased belief by an adjusted 11.9 points and decreased it by 12.9 in the opposite condition.","The earlier cached FAR abstract instead describes three studies, N=2,724; these versions have different denominators."],"interpretation":"NUWA interpretation: persuasive impact can favor false claims under the tested instructions; study versions must remain separate.","limits":"US online samples and immediate self-report outcomes limit population and long-term inference; cross-study comparisons are not all randomized contrasts.","decisionRelevance":"Model recommendation: cite arXiv:2601.05050v3 explicitly and preserve truth-constraint and debriefing conditions.","takeaway":"NUWA interpretation: persuasive impact can favor false claims under the tested instructions; study versions must remain separate.","connections":[],"scoring":"Pre/post 0–100 conspiracy-belief change; study arms and debriefing outcomes are separate.","conditions":"New arXiv v3 has four studies N=3,996; older FAR abstract has three studies N=2,724.","statistics":"Standard GPT-4o in Study 2 increased belief by an adjusted 11.9 points and decreased it by 12.9 in the opposite condition. The earlier cached FAR abstract instead describes three studies, N=2,724; these versions have different denominators.","usage":"Model recommendation: cite arXiv:2601.05050v3 explicitly and preserve truth-constraint and debriefing conditions."},"zh":{"question":"对话AI能否提高阴谋信念，真实性约束能否缓解？","design":"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。","findings":["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。"],"interpretation":"NUWA 解读：受测指令下说服力可支持错误信息，版本必须分开。","limits":"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。","decisionRelevance":"模型建议：明确引用2601.05050v3，保留真实性约束及纠正告知条件。","takeaway":"NUWA 解读：受测指令下说服力可支持错误信息，版本必须分开。","connections":[],"scoring":"0–100信念前后变化，分别保留实验组及纠正告知后结果。","conditions":"新v3为四研究3,996人，旧FAR摘要为三研究2,724人。","statistics":"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。","usage":"模型建议：明确引用2601.05050v3，保留真实性约束及纠正告知条件。"},"assertionPolicy":{"question":"source-research-question","design":"source-finding","findings":"source-finding","interpretation":"nuwa-interpretation","limits":"source-limit-or-explicit-editorial-scope-limit","connections":"source-supported-relationship-only","decisionRelevance":"model-recommendation-not-source-conclusion"},"sourceRefs":[{"sourceId":"conspiracy-persuasion","url":"https://arxiv.org/html/2601.05050v3","locator":"arXiv:2601.05050v3 (15 July 2026); Methods: Overview of study designs","version":"Selected original resource https://arxiv.org/html/2601.05050v3; sha256:506938d68312ef63bdfbf4af97aa957f36c05f92299ff1032732f0ecc2bc8f12; archived source capture; exact capture time not independently established","artifactSha256":"506938d68312ef63bdfbf4af97aa957f36c05f92299ff1032732f0ecc2bc8f12"},{"sourceId":"conspiracy-persuasion","url":"https://arxiv.org/html/2601.05050v3","locator":"Methods: Participants, recruitment and sample sizes","version":"Selected original resource https://arxiv.org/html/2601.05050v3; sha256:506938d68312ef63bdfbf4af97aa957f36c05f92299ff1032732f0ecc2bc8f12; archived source capture; exact capture time not independently established","artifactSha256":"506938d68312ef63bdfbf4af97aa957f36c05f92299ff1032732f0ecc2bc8f12"},{"sourceId":"conspiracy-persuasion","url":"https://arxiv.org/html/2601.05050v3","locator":"Methods: Measures and outcomes","version":"Selected original resource https://arxiv.org/html/2601.05050v3; sha256:506938d68312ef63bdfbf4af97aa957f36c05f92299ff1032732f0ecc2bc8f12; archived source capture; exact capture time not independently established","artifactSha256":"506938d68312ef63bdfbf4af97aa957f36c05f92299ff1032732f0ecc2bc8f12"},{"sourceId":"conspiracy-persuasion","url":"https://arxiv.org/html/2601.05050v3","locator":"Results: Can design interventions mitigate the bunking effect?","version":"Selected original resource https://arxiv.org/html/2601.05050v3; sha256:506938d68312ef63bdfbf4af97aa957f36c05f92299ff1032732f0ecc2bc8f12; archived source capture; exact capture time not independently established","artifactSha256":"506938d68312ef63bdfbf4af97aa957f36c05f92299ff1032732f0ecc2bc8f12"},{"sourceId":"conspiracy-persuasion","url":"https://arxiv.org/html/2601.05050v3","locator":"Discussion","version":"Selected original resource https://arxiv.org/html/2601.05050v3; sha256:506938d68312ef63bdfbf4af97aa957f36c05f92299ff1032732f0ecc2bc8f12; archived source capture; exact capture time not independently established","artifactSha256":"506938d68312ef63bdfbf4af97aa957f36c05f92299ff1032732f0ecc2bc8f12"}],"publication":{"state":"published","publishedOn":"2026-10-29","authorizationReference":"user-authorized-global-index-publication-2026-10-29","reviewedCandidateId":"local-6efe31bc4a0c0f37"},"review":{"state":"published-note","sourceScope":"full-text","availableScope":null,"readingCoverage":"selected-source-sections; not a complete section-by-section paper audit","completePaperAudit":false,"independentEvidenceReview":"pending-human-review","bilingualEditorialReview":"pending-human-review","authorship":"model-assisted-research-note","preparedFingerprint":"8807863e273581df13ffc09555ad24dca7214c63164e4ddbb9c764d32e941c6e"}},"sources":[{"id":"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","version":"FAR.AI report 2026-01-08","publicationDate":"2026-01-08","datePrecision":"day","type":"controlled-study","verifiedOn":"2026-10-25","documentId":"https://www.far.ai/research/large-language-models-can-effectively-convince-people-to-believe-conspiracies"}]}