Disinformation Detection: An Evolving Challenge in the Age of LLMs
Bohan Jiang Zhen Tan Ayushi Nirmal Huan Liu
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.