用大模型构建生成对抗网络，自动产假新闻并解释真伪。
LLM-GAN: Construct Generative Adversarial Network Through Large Language Models For Explainable Fake News Detection
- 让大模型当生成器和检测器，自动生成假新闻并判断真假。
- 生成的假新闻逼真度高，解释内容准确且有逻辑。
- 适合需要可解释性检测的新闻平台或云服务部署。
可解释的假新闻检测需对新闻真实性进行预测并提供标注解释。当前大型语言模型（LLMs）虽具备强大的自然语言理解与解释生成能力，但在该任务中仍面临两大挑战：一是假新闻看似合理，易误导大模型，使其难以理解复杂的造谣机制；二是使用大模型时会产生正确与错误的解释，需大量人工校验。本文提出 LLM-GAN 框架，通过提示工程使大模型同时担任生成器与检测器，实现逼真的假新闻生成与检测。实验表明，该框架在预测性能与解释质量上均表现优异。进一步，我们将 LLM-GAN 集成至云原生 AI 平台，为云端假新闻检测服务提供更高效支持。
原文摘要 · Abstract (English)
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.
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