LLM-GAN: Construct Generative Adversarial Network Through Large Language Models For Explainable Fake News Detection

TL;DR

This paper introduces LLM-GAN, a framework leveraging large language models as generator and detector to improve explainable fake news detection, addressing challenges of misleading content and explanation accuracy.

Contribution

The paper presents a novel LLM-GAN framework that uses prompting mechanisms for realistic fake news generation and detection, enhancing prediction and explanation quality.

Findings

Effective fake news detection performance

High-quality, explainable predictions

Successful integration into cloud platform

Abstract

Explainable fake news detection predicts the authenticity of news items with annotated explanations. Today, Large Language Models (LLMs) are known for their powerful natural language understanding and explanation generation abilities. However, presenting LLMs for explainable fake news detection remains two main challenges. Firstly, fake news appears reasonable and could easily mislead LLMs, leaving them unable to understand the complex news-faking process. Secondly, utilizing LLMs for this task would generate both correct and incorrect explanations, which necessitates abundant labor in the loop. In this paper, we propose LLM-GAN, a novel framework that utilizes prompting mechanisms to enable an LLM to become Generator and Detector and for realistic fake news generation and detection. Our results demonstrate LLM-GAN's effectiveness in both prediction performance and explanation quality. We further showcase the integration of LLM-GAN to a cloud-native AI platform to provide better fake news detection service in the cloud.