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.

大语言模型（LLM）的出现具有革命性的影响。然而，像 ChatGPT 这样的 LLM 是否可能被滥用来生成误导信息，对在线安全和公众信任构成了严重关注。我们从检测难度的角度提出了一个基本的研究问题：LLM 生成的误导信息是否比人类编写的误导信息具有更大的危害性？通过我们的实证研究，我们发现相比于具有相同语义的人类编写的误导信息，LLM 生成的误导信息对于人类和检测器来说更难以检测，这表明它可能具有更具欺骗性的风格，并有可能造成更大的伤害。我们还讨论了我们发现对抗 LLM 时的误导信息在信息时代和相应对策的影响。