Chen suggests that LLMs can have more deceptive styles than human authors due to their strong capacity to follow user instructions. The researchers argue that the difficulty in detecting LLM-authored misinformation means it can cause greater harm, posing serious threats to online safety and public trust. They call for collective efforts from various stakeholders to combat LLM-generated misinformation.
Key takeaways:
- Misinformation generated by large language models (LLMs) is more difficult to detect than false claims created by humans, according to researchers Canyu Chen and Kai Shu.
- The researchers examined whether LLM-generated misinformation can cause more harm than human-generated infospam, using eight LLM detectors to evaluate human and machine-authored samples.
- LLMs can use four types of controllable misinformation generation prompting strategies to craft misinformation, and can also be instructed to write an arbitrary piece of misinformation without a reference source.
- The difficulty of detecting LLM-authored misinformation means it can do greater harm, posing serious threats to online safety and public trust.