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Learning Whom to Trust About the Unseen: Source Credibility and Reliability Transfer in Language Models — arXiv:2611.08855

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.08855
License (as archived): CC0-1.0
Archived: 2026-11-09T22:33:46.872Z
SHA-256 of the archived text: 14183a5200a9b5ed9653bf42e3faa38ea4099654e49b59772f423f093c2361da
Stable URL of this page: https://metraipolis.com/documento/dc_f1d68752bbdd

How to cite

Learning Whom to Trust About the Unseen: Source Credibility and Reliability Transfer in Language Models — arXiv:2611.08855 — arXiv (en), https://arxiv.org/abs/2611.08855 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f1d68752bbdd (Metraipolis archive dc_f1d68752bbdd, sha256 14183a5200a9b5ed9653bf42e3faa38ea4099654e49b59772f423f093c2361da)

BibTeX

@misc{dcf1d68752bbdd,
  title = {Learning Whom to Trust About the Unseen: Source Credibility and Reliability Transfer in Language Models — arXiv:2611.08855},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.08855},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f1d68752bbdd; sha256 14183a5200a9b5ed9653bf42e3faa38ea4099654e49b59772f423f093c2361da},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.08855. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.08855 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-09T22:30:47.257Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).