LLM-GAN: Constructing Generative Adversarial Network Through Large Language Models for Explainable Fake News Detection
Yifeng Wang, Zhouhong Gu, Siwei Zhang, Suhang Zheng, Tao Wang, Tianyu Li, Hongwei Feng, Yanghua Xiao
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, using 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 correct and incorrect explanations, requiring abundant labor in the loop. In this paper, we propose LLM-GAN, a novel framework that utilizes prompting mechanisms to enable an LLM to function as a Generator and a Detector for realistic fake news generation and detection. Extensive experimental results demonstrate LLM-GAN’s effectiveness in both prediction performance and explanation quality.
BibTeX
@inproceedings{icassp2025_llmganconstructi,
title = {LLM-GAN: Constructing Generative Adversarial Network Through Large Language Models for Explainable Fake News Detection},
author = {Yifeng Wang and Zhouhong Gu and Siwei Zhang and Suhang Zheng and Tao Wang and Tianyu Li and Hongwei Feng and Yanghua Xiao},
booktitle = {ICASSP 2025},
year = {2025}
}