Information Discernment in Large Language Models
A new study reveals that large language models (LLMs) struggle significantly with information discernment: they perform near chance at distinguishing reliable from unreliable sources and at updating beliefs toward the truth. The Learn2Discern framework, tested on 13 models and nearly 670K trials, shows models rely twice as much on source popularity as on reliability and update equally whether a claim improves or worsens accuracy. A user study (n=299) confirms these failures reduce trust and usage intent. Simple inference-time interventions can partially improve both forms of discernment.
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[Submitted on 22 May 2026]
Title:Information Discernment in Large Language Models
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Abstract:LLMs are increasingly used with external knowledge sources like the internet. Do they weigh information appropriately -- updating more for reliable sources (source discernment) and more when claims bring priors closer to the truth (truth discernment)? We formalize this as information discernment and introduce Learn2Discern (L2D), an experimental framework and benchmark grounded in three normative axioms with interpretable metrics. To establish external validity, a pre-registered, quota-matched user study (n=299) confirms that real LLM users endorse all three axioms and report that violations reduce their trust and usage intent. Across 13 models and nearly 670K trials, we find consistent failures across both dimensions: models perform near chance on source and truth discernment, rely on source popularity twice as much as source reliability, and update roughly equally whether a claim improves or worsens their position relative to the ground truth. Models integrate external knowledge most effectively on datasets where their priors are already the most accurate. Newer and larger models improve truth discernment but not source discernment, a blind spot that model complexity does not address. We identify simple inference-time interventions that improve both forms of discernment. We release our dataset and survey as a testbed for a core alignment property that scales in importance as LLMs replace traditional search.
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY)
Cite as: arXiv:2607.19355 [cs.AI]
(or arXiv:2607.19355v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.19355
arXiv-issued DOI via DataCite
Submission history
From: Joshua Ashkinaze [view email] [v1] Fri, 22 May 2026 12:13:16 UTC (2,982 KB)
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