Disinformation Detection: An Evolving Challenge in the Age of LLMs
September 25, 2023 ยท Declared Dead ยท ๐ SDM
"No code URL or promise found in abstract"
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Authors
Bohan Jiang, Zhen Tan, Ayushi Nirmal, Huan Liu
arXiv ID
2309.15847
Category
cs.CL: Computation & Language
Cross-listed
cs.AI,
cs.CY
Citations
72
Venue
SDM
Last Checked
4 months ago
Abstract
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.
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