A structured survey of AI hallucinations, synthesizing prior research to clarify their evolving definitions, underlying causes, and implications for Information Systems positions AI hallucinations as socio-technical phenomena with direct implications for trust, decision-making, and governance.
The generative artificial intelligence (AI) has been widely used in academic writing, and its hallucination issue becomes problematic when considering the accuracy and credibility of academic writing. The systematic literature review methodology is used in this paper, in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) process to filter articles related to the topic since 2022, with the aim of examining the influence of AI hallucinations on academic writing. The study reveals that AI hallucinations have three kinds of manifestations, which include content distortion, evidence failure and improper argumentation, which comprises factual error, false or irrelevant citation, and superficially coherent arguments lacking evidence. They may destroy the authenticity, transparency, and logic of academic writing. Even if technologies like Retrieval-Augmented Generation (RAG) and Meta-RAG were able to reduce the hallucination effect at least partially, it would not be possible to completely get rid of it. AI need to be considered as an additional tool, and people are still responsible to verify the information and adhere to academic standards. It is crucial to note that developing critical use skills of AI-generated texts by students in educational environments is very important.
The advent of generative AI (GenAI) has introduced a new challenge to legal practice: the proliferation of ‘hallucinations’, plausible-sounding yet fabricated legal content, including citations, facts, and legal rules. This article delves into the nature of artificial intelligence (AI)-driven hallucinations, examining their technical origins and the many reasons that can make legal professionals susceptible to these errors. It presents novel empirical data from a comprehensive database of adjudicated cases, charting the upward trajectory of this issue across diverse jurisdictions, and argues that hallucinations represent more than mere inaccuracies; they constitute a profound epistemic challenge. Far from being spared, international dispute settlement presents unique vulnerabilities to this challenge. Accordingly, the article proposes a pragmatic, multi-stage framework specifically tailored for international courts and arbitral tribunals to identify, manage, and respond to AI-generated falsehoods. This framework emphasizes procedural fairness, a factor-based assessment of culpability and impact, and a graduated range of remedial measures available to adjudicators. Ultimately, the article advocates for principled and cautious engagement with GenAI in international dispute resolution.
D. Charlotin· Journal of International Dis...· 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
The lifecycle of hallucination in LLMs is a concept that enables building solid frameworks on the control and reliability of LLMs in high-stakes environments, including health, legal, and scientific research. Although previous surveys have primarily focused on detection or mitigation, this survey provides a lifecycle-based overview of the hallucinations in the LLMs, their cause, detection, mitigation, and prevention. We propose a threefold categorization of hallucinations across the LLM lifecycle: data-related, training-related, and inference-related, which is consistent with the lifecycle of the development of the LLM. Each of these stages is discussed regarding the cause of hallucinations, their detection, and the ways they can be addressed under specific mitigation or prevention interventions. In addition, we discuss the available benchmark data using a number of parameters so as to establish their suitability in identifying, restricting and managing hallucinations. The survey provides researchers and practitioners with a standardized framework to understand, diagnose, and cure hallucinations in a systematic system to present actionable data to build safer and more reliable LLMs.
Naveen Lamba, Sanju Tiwari, Manas Gaur· International Journal of Dat...· 0 citations
A concise two-axis framework that integrates an “intrinsic-extrinsic” distinction in source attribution introduced by Ji et al. with a “faithfulness-factuality” distinction in contextual grounding surveyed is presented, yielding four clearly defined hallucination types applicable across tasks, modalities and architectures.
Misbah Khan, Preston Billion-Polak, T. Khoshgoftaar· IEEE Access· 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
