FactualRAG: a RAG-Powered framework for LLM fact-checking via IFCN-Accredited sources
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Retrieval quality, more than downstream reasoning, drives factual accuracy in retrieval-augmented verification.
“This restriction is motivated by evidence that retrieval quality, more than downstream reasoning, drives factual accuracy in retrieval-augmented verification (Chang & Lai, 2026)”
From Multi-Agent debate system based on large language models: structured deliberation and validation in satellite communications (2026) · cites this paper as Chang & Lai, 2026
Multi-agent debate improves verification only when evidence coverage is strong and can degrade it otherwise.
“where multi-agent debate improves verification only when evidence coverage is strong and can degrade it otherwise (Chang & Lai, 2026).”
From Multi-Agent debate system based on large language models: structured deliberation and validation in satellite communications (2026) · cites this paper as Chang & Lai, 2026
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“In addition, our runtime analysis contributes to ongoing discussions on the accuracy–efficiency trade–off in large language model reasoning systems (Madaan et al., 2023; Wang et al., 2022).”
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Flagged first, then by citations
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Gómez Álvarez, S., Mozo Quesada, A., Navarro, T., Gálvez Rojas, S., & López Valverde, F. (2026) · Journal of Intelligent Information Systems · doi:10.1007/s10844-026-01086-z
“…is motivated by evidence that retrieval quality, more than downstream reasoning, drives factual accuracy in retrieval-augmented verification (Chang & Lai, 2026) Prior to embedding, mathematical expressions were transcribed to LaTeX to avoid tokenization artifacts and to improve model…”
MMRDoc: A multi-granularity multimodal RAG framework for long-document VQA
Xu, S., & Wang, H. (2026) · Journal of Intelligent Information Systems · doi:10.1007/s10844-026-01075-2
“…recent RAG studies have explored trustworthy information verification scenarios, including multilingual multimodal misinformation detection with RAG-enhanced LVLMs and LLMs (Harris et al., 2026 ), as well as factual verification grounded in accredited evidence sources (Chang & Lai, 2026).”
Zhang, T., Wu, J., & Kang, Y. (2026) · Journal of Intelligent Information Systems · doi:10.1007/s10844-026-01077-0
“FactualRAG grounds LLM-based fact-checking in evidence retrieved from IFCN-accredited sources, while M&M-RAG combines multilingual evidence retrieval with vision-language reasoning for cross-lingual multimodal mis-information detection (Chang and Lai, 2026; Harris et al., 2026).”
RCT-MARS: when per-query retrieval routing fails, and what it takes to succeed
Srivastava, A. (2026) · Journal of Intelligent Information Systems · doi:10.1007/s10844-026-01080-5
“In the same spirit of condition - ing downstream behaviour on retrieval trustworthiness, FactualRAG (Chang & Lai, 2026 ) grounds LLM fact-checking in retrieval from IFCN-accredited sources, using source cred - ibility rather than paradigm identity as the routing signal.”
Hastuti, Sultan, M. I., & Aziz, S. (2026) · SHS Web of Conferences · doi:10.1051/shsconf/202623902005
- Authors
- Chang, L.-J., & Lai, C.-M.
- Affiliations
- Tunghai University (TW)
- Corresponding author
- Lai, Chun-Ming
- ORCID
- Lai, Chun-Ming
- Venue
- Journal of Intelligent Information Systems
- Published
- 2026-03-28
- Volume
- 64
- Issue
- 4
- Pages
- 1687-1718
- ISSN
- 0925-9902 (ISSN-L)
- 1573-7675
- OpenAlex
- W7142225672
- Cited by
- 5
- References
- 44
- Citations by year
- 2026: 5
- FWCI
- 57.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Intelligent Information Systems (publishedVersion)
- Funders
- National Science and Technology Council (113-2221-E-029-009-MY3)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- fact-checking, retrieval-augmented generation, large language models, misinformation, hybrid retrieval, multi-agent debate, factual grounding, content moderation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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