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Cited by 148Misinformation and Its ImpactsSpam and Phishing DetectionTopic Modeling

Combating misinformation in the age of LLMs: Opportunities and challenges

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Chen, Canyu, Shu, Kai·2024·AI Magazine
DOI 10.1002/aaai.12188
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Abstract

Misinformation such as fake news and rumors is a serious threat for 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 emerging question is: can we 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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  • Even aligned LLMs are still vulnerable to adversarial attacks, typically called the jailbreak issue.

    “recent works show that even aligned LLMs are still vulnerable to adversarial attacks, typically called the jailbreak issue of LLMs [46,37,15,2].”

    From Jailbreak and Guard Aligned Language Models With Only Few In-Context Demonstrations (2026) · cites this paper as [2]

  • Verifiable-ML frameworks such as ZKML, FairZK, and FairProof extend privacy guarantees to model behavior.

    “Verifiable-ML frameworks -ZKML (Chen et al., 2024), FairZK (Zhang et al., 2025b), and FairProof Yadav et al. (2024)-extend these guarantees to model behavior.”

    From Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents (2025) · cites this paper as Chen et al., 2024

    The cited paper’s “Trustworthy misinformation detection” section mentions privacy as an aspect of trustworthy detection but does not discuss ZKML or verifiable-ML guarantees for model behavior. Searches for ZKML, zero-knowledge machine learning, and cryptographic verification found no supporting passage in our copy. (LLM, conf=0.75)

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Authors
Chen, C., & Shu, K.
Affiliations
Illinois Institute of Technology (US)
Corresponding author
Shu, Kai
ORCID
Venue
AI Magazine
Publisher
Wiley
Published
2024-08
Volume
45
Issue
3
Pages
354-368
ISSN
  • 0738-4602 (ISSN-L)
  • 2371-9621
DOI
10.1002/aaai.12188
OpenAlex
W4401213616
Cited by
148
References
108
Citations by year
2026: 46 · 2025: 71 · 2024: 31
FWCI
106.16
Type
journal article
Language
en
Open access
Licence
Locations
  • AI Magazine (publishedVersion, cc-by-nc-nd)
  • arXiv (Cornell University) (submittedVersion, other-oa)
  • arXiv (Cornell University)
Funders
  • U.S. Department of Homeland Security (17STQAC00001‐07‐04)
  • Office of the Director of National Intelligence
  • Intelligence Advanced Research Projects Activity
  • National Science Foundation (SaTC‐2241068, IIS‐2339198, POSE‐2346158)
Topics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Keywords
misinformation, large language models, fake news, rumors, misinformation detection, misinformation countermeasures, public trust

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