Fake news detection by large language models

Section: Artificial Intelligence in Media and Communication Studies

The article is based on the results of a study evaluating the ability of large language models (LLMs) to distinguish between reliable and false news. While specialized fact-checking organizations are capable of conducting thorough investigations using substantial resources, ordinary readers typically lack access to such powerful tools. Instead, they assess the credibility of information based on personal experience, the opinions of their social environment, and increasingly, the output of publicly accessible LLMs. The study revealed that LLMs are highly accurate in identifying reliable news as such; however, they frequently make errors when classifying false news. The research also examined the capacity of LLMs to revise false news items in ways that make them appear more credible.

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To cite this article: Leonova Iu. S., Fedyanin D. N., Chkhartishvili A. G. (2025) Raspoznavaniye fal’shivykh novostey bol’shimi yazykovymi modelyami [Fake news detection by large language models]. Vestnik Moskovskogo Universiteta. Seriya 10. Zhurnalistika 5: 233–247. DOI: 10.55959/msu.vestnik.journ.5.2025.233247