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Disinformation Detection: An Evolving Challenge in the Age of LLMs

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arxiv 2309.15847 v1 pith:DD3GKZLC submitted 2023-09-25 cs.CL cs.AIcs.CY

classification cs.CLcs.AIcs.CY
keywords disinformationdetectionllmsadvancementsmodelsresearchwhatacross
verification ladder T0 review T1 audit T2 compute T3 formal

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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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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Fake News Detection After LLM Laundering: Measurement and Explanation

    cs.CL 2025-01 conditional novelty 5.0 of 10

    LLM paraphrasing of fake news degrades detector performance across 17 detectors, with Pegasus evading best and a sentiment shift that BERTScore fails to capture.

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