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Combating Misinformation in the Age of LLMs: Opportunities and Challenges

Paper · 2023 · Canyu Chen and Kai Shu

Summary

This survey asks how large language models change the fight against false content such as fake news and rumors. The authors treat LLMs as cutting both ways. Their broad world knowledge and reasoning skills could improve detection, intervention and attribution. Yet the same models make it cheap to mass-produce convincing falsehoods, whether through hallucination or deliberate misuse.

The paper offers no new dataset or model. It first sorts pre-LLM detection research into seven categories. It then reviews recent work on using LLMs as detectors, which is still mostly about detection, and sketches future directions such as multimodal LLMs, LLM agents and human–LLM collaboration. Finally, it looks at the opposite problem: how to characterize and counter misinformation that LLMs themselves produce.

The central concern, drawn from prior work, is that machine-written misinformation can fool both people and automated detectors more easily than human-written text with the same meaning. The authors conclude that technical fixes alone are not enough. They call for coordinated action across several disciplines and stakeholder groups, which makes the survey a useful map for NLP teams working on fact-checking.

Key points

  • Pre-LLM detection methods fall into seven groups:
    • linguistic features (e.g., LIWC, MTLD, TTR)
    • neural models (LSTM, CNN, BERT/FakeBERT)
    • social context, often handled with GNNs
    • external knowledge (knowledge graphs, evidence texts)
    • generalization under domain and temporal shift
    • low-supervision learning
    • multilingual and multimodal fusion
  • The survey is organized around two questions: can LLMs help combat misinformation, and how should LLM-generated misinformation be countered?
  • Emerging risks include:
    • realistic multimodal fakes built with Midjourney, Stable Diffusion and GPT-4V, illustrated by a fabricated "Pentagon explosion" post
    • autonomous misinformation agents, since safety guards can be removed through fine-tuning
    • AI-driven attacks on people's cognitive security
  • The proposed response spans technology, sociology, psychology, education and policymaking, including regulation of both closed models (ChatGPT, GPT-4) and open models (Llama2, Mistral).
  • Additional resources are collected at llm-misinformation.github.io.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

 Fields, topics and concepts

Cite

Cite as Canyu Chen and Kai Shu. 2023. Combating Misinformation in the Age of LLMs: Opportunities and Challenges. arXiv:2311.05656.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2311.05656 · arXiv 2311.05656

@misc{chen2023combating,
  title = {{Combating Misinformation in the Age of LLMs: Opportunities and Challenges}},
  author = {Canyu Chen and Kai Shu},
  year = {2023},
  doi = {10.48550/arxiv.2311.05656},
  eprint = {2311.05656},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2311.05656}
}

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