Large Language Models Hallucination: A Comprehensive Survey

Aisha Alansari, Hamzah Luqman

Abstract

Large language models (LLMs) have transformed natural language processing, achieving remarkable performance across diverse tasks. However, their impressive fluency often comes at the cost of producing false or fabricated information, a phenomenon known as hallucination. Hallucination refers to the generation of content by an LLM that is fluent and syntactically correct but factually inaccurate or unsupported by external evidence. Hallucinations undermine the reliability and trustworthiness of LLMs, especially in domains requiring factual accuracy. This survey provides a comprehensive review of research on hallucination in LLMs, with a focus on causes, detection, and mitigation.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper

Eigenvector Research — Marco van Hurne

How this paper contributes to solving the Zone III problem (PASF-PADE)

This paper directly addresses one of the core structural challenges in Zone III deployments. The research on LLMs, Hallucination, Hallucination Causes provides evidence-based foundations that enterprise architects cannot ignore when designing long-horizon autonomous workflows. The findings challenge the assumption that a base language model — however capable — can handle the complexity of durable, governed, multi-step execution without explicit architectural intervention. For Zone III practitioners, this paper belongs in the required reading list.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Topics

LLMsHallucinationHallucination CausesHallucination DetectionHallucination Mitigation

Relevance Scores

Long-Horizon Score85

Enterprise Score80

Completeness75

Paper Info

Year2025

Venue

Type

ChapterCh. 10

Authors2

Zone III Analysis