Use of Retrieval-Augmented Large Language Model for COVID-19 Fact-Checking: Development and Usability Study
The integration of RAG systems with LLMs substantially improves the accuracy and contextual relevance of automated fact-checking. By reducing hallucinations and enhancing transparency by citing retrieved sources, this method holds significant promise for rapid, reliable information verification to combat misinformation during public health crises.
Authors
- Ruopeng An (ORCID: https://orcid.org/0000-0001-9632-0209)
- Yuyi Yang (ORCID: https://orcid.org/0000-0002-7625-810X)
- Mengmeng Ji (ORCID: https://orcid.org/0000-0002-1205-0369)
- Jingyi Huang (ORCID: https://orcid.org/0000-0003-4555-0797)
- Hai Li (ORCID: https://orcid.org/0000-0002-2510-5103)
Institutions
- Western New Mexico University (US)
- Shanghai University of Sport (CN)
- Washington University in St. Louis (US)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-02-28
- DOI
- https://doi.org/10.2196/66098
- Citations
- 10
- Primary Topic
- Misinformation and Its Impacts
- Type
- article
- Field-Weighted Citation Impact
- 42.02