Information Discernment in Large Language Models
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.
- ▪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.
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Computer Science > Artificial Intelligence arXiv:2607.19355 (cs) [Submitted on 22 May 2026] Title:Information Discernment in Large Language Models Authors:Joshua Ashkinaze, Laura Kurek, Alina Faisal, Tongyuan Miao, Mariam Joseph, Ceren Budak, Eric Gilbert View a PDF of the paper titled Information Discernment in Large Language Models, by Joshua Ashkinaze and 6 other authors View PDF HTML (experimental) Abstract:LLMs are increasingly used with external knowledge sources like the internet.
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