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

TL;DR

This paper compares the effectiveness of several large language models in detecting fake news, highlighting their capabilities and limitations to inform future AI-driven misinformation mitigation strategies.

Contribution

It provides a comprehensive comparative analysis of multiple LLMs' performance in fake news detection using real-world datasets.

Findings

GPT-4 outperforms smaller models in accuracy

Large models show better contextual understanding

Limitations identified in detecting subtle misinformation

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.