Can LLM-Generated Misinformation Be Detected?
This addresses the problem of online safety and public trust by showing that LLM-generated misinformation poses a greater detection challenge, which is incremental as it builds on existing concerns about AI-generated content.
The paper investigates whether LLM-generated misinformation is harder to detect than human-written misinformation, finding through empirical study that it is more deceptive and potentially more harmful for both humans and detectors.
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.