Do Large Language Models Hallucinate Electric Fata Morganas?

PROBLEMS WITH AI TRAINING: SOURCES AND CONSEQUENCES OF HALLUCINATION IN NEURAL NETWORK MODELS

Contemporary AI language models are increasingly deployed in safety-critical contexts, yet they remain prone to generating dangerous or factually incorrect outputs. Documented examples include AI-generated recipes suggesting the use of chlorine-producing ingredient combinations [1]. Rather than acknowledging these as model errors, developers have characterized them as “hallucinations”—a term that obscures the underlying technical causes. This paper investigates the structural and methodological sources of hallucination in neural network models, examines three predominant training paradigms, and argues that incomplete training data, flawed train–test partitioning, and the inaccessibility of expert tacit knowledge are root causes of unreliable AI outputs. Recommendations for more rigorous training methodologies are proposed.

Survey of AI Hallucinations and Mitigation Survey of AI Hallucinations and Mitigation

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.

AI Hallucinations in Academic Writing and Addresses

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.

An epistemological study of ‘AI psychosis’

How Much Do Legal RAG Systems Still Hallucinate?

Hallucination is a major challenge for retrieval-augmented generation (RAG) systems in the legal domain, where ungrounded answers can lead to serious consequences. To better understand this problem, we conduct a fine-grained analysis of hallucination behavior in eight legal RAG systems across two legal corpora, the GDPR (in English) and a national civil law (in French). Using claim-level and answer-level evaluation, we report on hallucination density and severity, analyze performance across question categories and user personas, and validate our findings on an independent set of 142 legal-expert-authored questions. Our results show that hallucinations remain pervasive, ranging from less than 10% of responses for the best-performing systems to nearly half in the worst case. We further find that false-premise questions, containing incorrect assumptions that must be rejected, produce high hallucination rates on the manually-drafted questions.

AI Hallucination as Epistemic Proliferation

The Epistemic Proliferation Model is conceptualised as an ethical failure of restraint, that is, the tendency of fluent models to keep producing confident, expert-sounding language after evidential grounding has become weak or unverifiable.