# 学习信任谁来判断未见之事：语言模型中的来源可信度与可靠性迁移

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**Published:** 2026年11月9日
**Author:** Ruijia Guan
**Source:** arXiv — cs.AI
**Language:** en-US
**HTML:** https://aiqazaqstan.com/zh/articles/learning-whom-to-trust-about-the-unseen-source-credibility-and-reliabili

arXiv:2611.08855v1 通告类型：新 摘要：以前正确过的来源值得关注，但关注什么？我们询问当新主张无法验证时，语言模型中经证实的可靠记录能延伸多远，以及答案是否取决于类型...

arXiv:2611.08855v1 Announce Type: new Abstract: A source who has been right before deserves attention, but about what? We ask how far a demonstrated record of reliability travels in language models when the new claim cannot be checked, and whether the answer depends on the kind of claim. Three behavioral studies across six models approach the question from three angles. Study 1 adapts the Einstein-effect paradigm and finds that models, unlike the human sample, favor a spiritual leader over a scientist for the same obscure statement, and that the preference largely reverses when the statement is reworded in the vocabulary of physics. Study 2 adapts a paradigm in which a trusted expert calls a scientific or religious explanation unknown or a mystery; models reproduce the human domain asymmetry in exaggerated form. Study 3, the central test, gives two fictional informants different records on checkable river forecasts and then has them endorse matched claims about hidden natural and spiritual causes of the same village events. Most models carry the record to natural causes and barely at all to spiritual ones; one model transfers it weakly to either. Telling the model that the reliable informant forecasts by spiritual guidance rather than by measurement lowers that informant's influence on natural claims in every model and produces no comparable gain on spiritual claims in any. Demonstrated reliability thus has a scope in these models, and the account a source gives of how they came to be reliable can narrow it. Whether this reflects a judgment about relevance or the low prior credence of spiritual claims is the question the design leaves open, and we outline the experiment that would settle it.

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*内容来自原始来源： [arXiv — cs.AI](https://arxiv.org/abs/2611.08855). 查看原文.*

**标签：** #arXiv

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