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FactualRAG: a RAG-Powered framework for LLM fact-checking via IFCN-Accredited sources

Li-Di Chang & Chun-Ming Lai

Journal of Intelligent Information Systems2026https://doi.org/10.1007/s10844-026-01041-yarticle
ABDC B
Weight
0.50

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https://doi.org/https://doi.org/10.1007/s10844-026-01041-y

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@article{li-di2026,
  title        = {{FactualRAG: a RAG-Powered framework for LLM fact-checking via IFCN-Accredited sources}},
  author       = {Li-Di Chang & Chun-Ming Lai},
  journal      = {Journal of Intelligent Information Systems},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1007/s10844-026-01041-y},
}

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FactualRAG: a RAG-Powered framework for LLM fact-checking via IFCN-Accredited sources

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.