Evaluating the Efficacy of Large Language Models in Detecting Fake News: A Comparative Analysis

Sahas Koka, Anthony Vuong, Anish Kataria
6/5/2024
cs.CLcs.AI

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Abstract

In an era increasingly influenced by artificial intelligence, the detection of fake news is crucial, especially in contexts like election seasons where misinformation can have significant societal impacts. This study evaluates the effectiveness of various LLMs in identifying and filtering fake news content. Utilizing a comparative analysis approach, we tested four large LLMs -- GPT-4, Claude 3 Sonnet, Gemini Pro 1.0, and Mistral Large -- and two smaller LLMs -- Gemma 7B and Mistral 7B. By using fake news dataset samples from Kaggle, this research not only sheds light on the current capabilities and limitations of LLMs in fake news detection but also discusses the implications for developers and policymakers in enhancing AI-driven informational integrity.

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Cite this paper

@article{koka2024evaluating,
  title  = {Evaluating the Efficacy of Large Language Models in Detecting Fake News: A Comparative Analysis},
  author = {Sahas Koka and Anthony Vuong and Anish Kataria},
  year   = {2024},
  eprint = {2406.06584},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2406.06584v1}
}

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