The results suggest that no single architecture guarantees factual reliability, however, contextual grounding and verification mechanisms can significantly improve response quality and highlight the importance of combining language modelling capabilities with grounding strategies to support the development of more reliable AI systems.
This review paper provides a comprehensive overview of hallucinations in GAI and LLMs, and synthesizes a range of correction and mitigation techniques, from proactive measures during training to hybrid approaches that combine detection and intervention.
M. Naser· Language Resources and Evalu...· 0 citations
This study proposes a novel conceptual framework and taxonomy for hallucination mitigation in low-code AI environments, integrating retrieval, validation, conflict resolution, and workflow orchestration mechanisms to contribute to the development of more reliable, transparent, and scalable AI systems.
I. K. W. Adnyana, Rosalin Theophilia Tayane, Fahmi Fahmi et al.· EDUKASIA Jurnal Pendidikan d...· 0 citations
This survey provides a comprehensive treatment of the field across five interconnected dimensions, proposing a unified five-class taxonomy that organizes hallucinations by their failure mode: object, attribute, relational, factual, factual, and reasoning.
A. O. Ogar, Joshua Abah, M. Suleiman et al.· 0 citations
HallDetect, a lightweight, reference-free, and black-box framework for hallucination detection, is presented, a lightweight, reference-free, and black-box framework for hallucination detection that is evaluated not only on summarization but across a broader range of source-grounded generation settings.
Achir Oukelmoun, N. Semmar, Gäel de Chalendar· 0 citations
This review provides systematic theoretical support for industrial RAG model selection and optimization and summarizes existing research gaps, including lightweight deployment and multimodal expansion, and proposes future research directions for trustworthy RAG systems.
Shujing Liu· Applied and Computational En...· 0 citations
Generative artificial intelligence has become an important technology for knowledge creation, education, healthcare, finance, and organizational decision-making. However, its practical adoption is limited by hallucinations, where generated responses contain fabricated, unsupported, outdated, contextually inappropriate, logically inconsistent, or incorrectly cited information. Existing verification approaches frequently assess responses independently, rely on imperfect evidence retrieval, and lack mechanisms for reusing previously verified knowledge, resulting in limited adaptability and explainability. This study proposes a Case-Based Verification Framework (CBVF) to improve the reliability of generated responses through experience-driven verification. The framework employs the four stages of Case-Based Reasoning—Retrieve, Reuse, Revise, and Retain—and integrates atomic-claim decomposition, semantic case retrieval, evidence entailment, source-reliability weighting, semantic-entropy estimation, hallucination-risk calibration, selective verification, explainable correction, and incremental case-base maintenance. The framework is evaluated using approximately 2,000 prompt–response instances constructed from six publicly available benchmark datasets spanning general knowledge, multi-hop reasoning, scientific verification, citation verification, temporal reasoning, and hallucination evaluation. Performance is compared with unverified generation, self-consistency verification, retrieval-augmented generation, and retrieval-augmented generation combined with rule-based fact-checking using claim groundedness, evidence support, calibration quality, hallucination reduction, selective verification, correction safety, retrieval effectiveness, and Bayesian multilevel analysis. The experimental evaluation demonstrates that the proposed framework consistently outperforms the baseline approaches by improving factual grounding, evidence alignment, calibration accuracy, and retrieval quality while substantially reducing hallucinations and preserving response relevance and semantic meaning. The Bayesian analysis further confirms statistically robust improvements across multiple benchmark domains. The findings indicate that integrating Case-Based Reasoning with evidence-driven verification provides an adaptive, explainable, and continuously improving mechanism for enhancing the trustworthiness of generative artificial intelligence in applications requiring reliable and evidence-supported information.
Thacha Lawanna· Journal of Intelligent Decis...· 0 citations