The advent of Large Language Models (LLMs) has made a transformative impact. However, the potential that LLMs such as ChatGPT can be exploited to generate misinformation has posed a serious concern to online safety and public trust. A fundamental research question is: will LLM-generated misinformation cause more harm than human-written misinformation? We propose to tackle this question from the perspective of detection difficulty. We first build a taxonomy of LLM-generated misinformation. Then we categorize and validate the potential real-world methods for generating misinformation with LLMs. Then, through extensive empirical investigation, we discover that LLM-generated misinformation can be harder to detect for humans and detectors compared to human-written misinformation with the same semantics, which suggests it can have more deceptive styles and potentially cause more harm. We also discuss the implications of our discovery on combating misinformation in the age of LLMs and the countermeasures.

翻译：大型语言模型（LLMs）的出现带来了变革性影响。然而，ChatGPT等LLMs可能被利用生成虚假信息，这对在线安全和公众信任构成了严重威胁。一个基础研究问题是：LLM生成的虚假信息是否比人类撰写的虚假信息造成更大危害？我们拟从检测难度的角度解决这一问题。首先构建LLM生成虚假信息的分类体系，进而分类并验证利用LLMs生成虚假信息的潜在真实世界方法。通过大量实证研究，我们发现：与语义相同的人类撰写的虚假信息相比，LLM生成的虚假信息对人类和检测器而言更难识别，这表明其可能具有更具欺骗性的风格并造成更大危害。本文还讨论了这一发现对LLM时代虚假信息治理及应对措施的启示。