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Combating Misinformation in the Age of LLMs: Opportunities and Challenges

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arxiv 2311.05656 v1 pith:HPQ74DFY submitted 2023-11-09 cs.CY

classification cs.CY
keywords misinformationllmscombatingcombateffortshandllm-generatedopportunities
verification ladder T0 review T1 audit T2 compute T3 formal
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Misinformation such as fake news and rumors is a serious threat on information ecosystems and public trust. The emergence of Large Language Models (LLMs) has great potential to reshape the landscape of combating misinformation. Generally, LLMs can be a double-edged sword in the fight. On the one hand, LLMs bring promising opportunities for combating misinformation due to their profound world knowledge and strong reasoning abilities. Thus, one emergent question is: how to utilize LLMs to combat misinformation? On the other hand, the critical challenge is that LLMs can be easily leveraged to generate deceptive misinformation at scale. Then, another important question is: how to combat LLM-generated misinformation? In this paper, we first systematically review the history of combating misinformation before the advent of LLMs. Then we illustrate the current efforts and present an outlook for these two fundamental questions respectively. The goal of this survey paper is to facilitate the progress of utilizing LLMs for fighting misinformation and call for interdisciplinary efforts from different stakeholders for combating LLM-generated misinformation.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 20 citations worldwide. Full citation record

  1. Communication is All You Need: Persuasion Dataset Construction via Multi-LLM Communication

    cs.CL 2025-02 conditional novelty 6.0 of 10

    A six-role multi-LLM communication framework generates persuasive dialogue data that human judges find nearly indistinguishable from human-written rewrites.

  2. Calibrated Selective Fact-Checking via Evidence Chain Evaluation

    cs.AI 2026-04 conditional novelty 4.0 of 10

    A tool-using LLM fact-checker that abstains on 6 of 95 claims reaches 97.8% accuracy on answered claims, but a simpler search baseline remains more accurate and better calibrated.

  3. Multi-agent Systems for Misinformation Lifecycle : Detection, Correction And Source Identification

    cs.MA 2025-05 reject novelty 3.0 of 10

    A conceptual multi-agent architecture for classifying, detecting, correcting, and sourcing misinformation is proposed but not implemented or evaluated.

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