Jul 2026· EDUKASIA Jurnal Pendidikan dan Pembelajaran· 0 citations· 36 references
TL;DR
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.
Abstract
This study systematically examines hallucination phenomena in Large Language Models (LLMs), focusing on their characteristics, causal factors, and mitigation strategies through Retrieval-Augmented Generation (RAG) and low-code orchestration platforms such as n8n. Using a Systematic Literature Review (SLR) approach based on PRISMA 2020 guidelines, this study analysed 40 peer-reviewed articles published between 2020 and 2025 from major scientific databases. The findings reveal that hallucinations are multidimensional, consisting of factual, semantic, and contextual hallucinations influenced by static training data, probabilistic token prediction, prompt ambiguity, and insufficient validation mechanisms. The review further demonstrates that RAG significantly improves factual accuracy by integrating external retrieval systems with LLM generation processes. Recent innovations such as Hybrid Retrieval and GraphRAG enhance contextual relevance and knowledge representation. A major finding of this study is the identification of “Conflict of Information” between external retrieved data and internal LLM knowledge in automated RAG pipelines. Furthermore, 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. These findings contribute to the development of more reliable, transparent, and scalable AI systems.
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
This study aims to examine the phenomenon of hallucinations in large language models (LLMs) within academic contexts, focusing on their manifestations, causes and implications for academic integrity, research quality and responsible artificial intelligence adoption in higher education.
A systematic literature review was conducted in accordance with PRISMA 2020 guidelines. Searches across Scopus, Web of Science and Emerald Insight databases using keywords related to AI hallucination and academic applications, of which 25 peer-reviewed journal articles met the inclusion criteria. Qualitative thematic analysis was performed using NVivo 14 to synthesise evidence on hallucination types, academic applications, impacts and mitigation strategies.
Six recurring types of hallucinations were identified, with fabricated or inaccurate citations emerging as the most prevalent. The findings indicate that hallucinations systematically compromise academic writing quality, distort assessment processes and undermine epistemic trust in scholarly outputs. Variation in hallucination rates across models and disciplines highlights their context-dependent nature. Key contributing factors include probabilistic text generation, limitations in training data, insufficient contextual understanding and the absence of robust verification mechanisms.
It further contributes a structured classification of hallucination types and a multi-layered governance approach to inform institutional policy and responsible AI adoption.
Addressing hallucinations in academic knowledge production is essential for preserving public trust in higher education and safeguarding the societal value of scholarly research.
This study advances existing knowledge by developing an integrated conceptual perspective linking hallucinations to epistemic risk, information integrity and digital trust.
K. Lai, N. Mustaffa, C. Preece et al.· Journal of Science and Techn...· 0 citations
Faithfulness hallucinations, where large language models generate outputs unsupported by retrieved evidence, remain a central challenge for trustworthy AI. We present a systematic empirical evaluation of faithfulness in retrieval-augmented generation (RAG) systems using two benchmark datasets, HotpotQA and HaluBench, covering both multi-hop reasoning and single-hop hallucination detection. We analyze three small-to-mid-sized (2B-8B) open-weight LLMs in combination with multiple retrieval strategies, including sparse, dense, and hybrid approaches, as well as score-based and rank-based fusion techniques, enabling a comprehensive assessment of retrieval-generation interactions. By disentangling retrieval and generation errors, we characterize how different pipeline components contribute to hallucinations in RAG systems. Our analysis provides actionable insights and practical evaluation protocols, highlighting the critical role of robust retrieval and careful system design. These findings offer a benchmarking-oriented perspective for developing more reliable and faithful RAG systems within evaluated model scales.
C. Mala, Gizem Gezici, Fosca Giannotti· Machine-mediated learning· 0 citations
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 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