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Cited by 39Misinformation and Its ImpactsTopic ModelingNatural Language Processing Techniques

Hallucination to truth: a review of fact-checking and factuality evaluation in large language models

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Rahman, Subhey Sadi, Islam, Md. Adnanul, Alam, Md. Mahbub, Zeba, Musarrat, Rahman, Md. Abdur, Chowa, Sadia Sultana, Raiaan, Mohaimenul Azam Khan, Azam, Sami·2026·Artificial Intelligence Review
DOI 10.1007/s10462-025-11454-w
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Abstract

Large language models (LLMs) are trained on vast and diverse internet corpora that often include inaccurate or misleading content. Consequently, LLMs can generate misinformation, making robust fact-checking essential. This review systematically analyzes how LLM-generated content is evaluated for factual accuracy by exploring key challenges such as hallucinations, dataset limitations, and the reliability of evaluation metrics. The review emphasizes the need for strong fact-checking frameworks that integrate advanced prompting strategies, domain-specific fine-tuning, and retrieval-augmented generation (RAG) methods. It proposes five research questions that guide the analysis of the recent literature from 2020 to 2025, focusing on evaluation methods and mitigation techniques. Instruction tuning, multi-agent reasoning, and RAG frameworks for external knowledge access are also reviewed. The key findings demonstrate the limitations of current metrics, the importance of validated external evidence, and the improvement of factual consistency through domain-specific customization. The review underscores the importance of building more accurate, understandable, and context-aware fact-checking. These insights contribute to the advancement of research toward more trustworthy models.

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  • Many citation-verification systems remain closed-source and proprietary, rendering their verification mechanisms opaque and their empirical performance irreproducible.

    “While a growing set of citation-verification systems has been developed to detect hallucinated citations in academic writing, many of these systems remain closed-source and proprietary, rendering their verification mechanisms opaque and their empirical performance irreproducible [20].”

    From CiteAudit: You Cited It, But Did You Read It? A Benchmark for Verifying Scientific References in the LLM Era (2026) · cites this paper as [20]

    Section 4.2.3 (p. 15) identifies Factcheck-GPT as a proprietary fact-checking model. It does not establish that many citation-verification systems for academic writing are closed-source, or that their mechanisms are opaque and their empirical performance irreproducible. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • Large language models hallucinate factual content even when surface fluency is maintained.

    “Large language models hallucinate factual content even when surface fluency is maintained, a failure mode characterized across model families, training regimes, and deployment settings in recent surveys [14, 29, 19] and in zero-resource detection work such as SelfCheckGPT [17].”

    From Source or It Didn't Happen: A Multi-Agent Framework for Citation Hallucination Detection (2026) · cites this paper as [19]

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Cited by

Sorted by citations

Authors
Rahman, S. S., Islam, M. A., Alam, M. M., Zeba, M., Rahman, M. A., Chowa, S. S., Raiaan, M. A. K., & Azam, S.
Affiliations
  • United International University (BD)
  • Daffodil International University (BD)
  • Monash University (AU)
  • Charles Darwin University (AU)
Corresponding author
  • Raiaan, Mohaimenul Azam Khan
  • Azam, Sami
ORCID
Venue
Artificial Intelligence Review
Publisher
Springer Science and Business Media LLC
Published
2026-01-03
Volume
59
Issue
2
ISSN
  • 0269-2821 (ISSN-L)
  • 1573-7462
DOI
10.1007/s10462-025-11454-w
OpenAlex
W7118132038
Cited by
39
References
14
Citations by year
2026: 39
FWCI
455.29
Type
journal article
Language
en
Open access
Licence
Locations
  • Artificial Intelligence Review (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Funders
Charles Darwin University
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
large language models, fact-checking, hallucinations, misinformation, evaluation metrics, retrieval-augmented generation, instruction tuning, multi-agent reasoning, domain-specific fine-tuning, external evidence, factual consistency

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.

What counts as a claim ›How we measure confidence ›How we check this ›

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