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Large Language Models Exhibit Human-Like Bayesian Hypocrisy — arXiv:2609.35779

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.35779
License (as archived): CC0-1.0
Archived: 2026-10-07T16:57:21.005Z
SHA-256 of the archived text: 0b6084b2126edc5c335d77254fc0ecf3fda00341dc2146d876c42a4aedf4e9e1
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How to cite

Large Language Models Exhibit Human-Like Bayesian Hypocrisy — arXiv:2609.35779 — arXiv (en), https://arxiv.org/abs/2609.35779 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ac0a486dffaf (Metraipolis archive dc_ac0a486dffaf, sha256 0b6084b2126edc5c335d77254fc0ecf3fda00341dc2146d876c42a4aedf4e9e1)

BibTeX

@misc{dcac0a486dffaf,
  title = {Large Language Models Exhibit Human-Like Bayesian Hypocrisy — arXiv:2609.35779},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.35779},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ac0a486dffaf; sha256 0b6084b2126edc5c335d77254fc0ecf3fda00341dc2146d876c42a4aedf4e9e1},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.35779. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Given recent achievements of large language models (LLMs), frontier models are expected to perform well on Bayesian reasoning tasks, at least as well as humans. Furthermore, there is no reason to expect that LLMs will condemn others who offer those very same Bayesian judgments, a fallibility observed in human decision-making (Cao, et al., 2019). In 5 experiments with 48 experimental conditions employing over 5,000 trials, GPT-4o and Claude 3.7 Sonnet were tested on two variations of a Bayesian reasoning task. We also assessed LLM evaluation of the competence and morality of a hypothetical person who had offered the same reasoning task as them. LLMs hovered near human performance on the Bayesian task, though their reasoning was more rule-based and rigid. Surprisingly, like humans but to a greater extent, LLMs also demonstrated the same hypocrisy in condemning others who, like them, had deployed Bayes' rule. In demonstrating Bayesian hypocrisy, LLMs highlight a humanlike error of a dissociation between self-performance and other-judgment, and caution against their use in domains where statistical fidelity and fairness norms collide.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-30 in cs.CL).
Categorie arXiv: Human-Computer Interaction (cs.HC) ; Computation and Language (cs.CL); Computers and Society (cs.CY).
Pagina del lavoro: https://arxiv.org/abs/2609.35779 — 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 2026-10-07T16:27:03.379Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-09-30?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).