Disinformation Capabilities of Large Language Models
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Ivan Vykopal, Matúš Pikuliak, Ivan Srba, Robert Moro, Dominik Macko, Maria Bielikova. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.
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Existing detection methods are mostly binary classifiers and cannot distinguish complex LLM-generated content such as mixed human-LLM input.
“current methods often focus on binary classification, failing to address the complexities of realworld scenarios like human-LLM collaboration.”
From Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement (2025) · cites this paper as [55]
The Detection methodology (page unavailable in our copy) says that all detectors evaluated in this study are binary classifiers. Section 7 describes testing them on fully LLM-generated and human-written articles, but the paper does not establish that current methods generally fail on mixed human–LLM content or human–LLM collaboration. (LLM, conf=0.85)
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Research works printed but not linked: 2
- Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1901.
- Ben Buchanan, Andrew Lohn, Micah Musser, and Kate- rina Sedova. 2021. Truth, lies, and automation. Cen- ter for Security and Emerging Technology, 1(1):2.
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- Authors
- Vykopal, I., Pikuliak, M., Srba, I., Moro, R., Macko, D., & Bielikova, M.
- Affiliations
- Brno University of Technology (CZ)
- Kempelen Institute of Intelligent Technologies (SK)
- Venue
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2024
- Pages
- 14830-14847
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4402684203
- Cited by
- 34
- References
- 0
- Citations by year
- 2026: 15 · 2025: 13 · 2024: 6
- FWCI
- 44.51
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- European Commission (101070093, 311070AKF2, 101079164, 101073921)
- HORIZON EUROPE Framework Programme
- European Regional Development Fund (311070AKF2)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- disinformation, large language models, misinformation detection
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