The advent of generative Large Language Models (LLMs) such as ChatGPT has catalyzed transformative advancements across multiple domains. However, alongside these advancements, they have also introduced potential threats. One critical concern is the misuse of LLMs by disinformation spreaders, leveraging these models to generate highly persuasive yet misleading content that challenges the disinformation detection system. This work aims to address this issue by answering three research questions: (1) To what extent can the current disinformation detection technique reliably detect LLM-generated disinformation? (2) If traditional techniques prove less effective, can LLMs themself be exploited to serve as a robust defense against advanced disinformation? and, (3) Should both these strategies falter, what novel approaches can be proposed to counter this burgeoning threat effectively? A holistic exploration for the formation and detection of disinformation is conducted to foster this line of research.

翻译：生成式大语言模型（LLMs）如ChatGPT的出现，在多个领域推动了变革性进展。然而，伴随这些进展，它们也带来了潜在威胁。其中一个关键担忧是虚假信息传播者滥用LLMs，利用这些模型生成极具说服力但具有误导性的内容，对虚假信息检测系统构成挑战。本文旨在通过回答三个研究问题来应对这一问题：（1）当前虚假信息检测技术在多大程度上能够可靠地检测LLM生成的虚假信息？（2）若传统技术效果不佳，能否利用LLMs本身作为应对高级虚假信息的稳健防御手段？（3）若上述两种策略均失效，可以提出哪些新颖方法来有效应对这一日益严峻的威胁？本文对虚假信息的形成与检测进行了整体性探索，以推动这一研究方向的发展。