Loki's Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Models
Chaozhuo Li, Pengbo Wang, Chenxu Wang, Litian Zhang, Zheng Liu, Qiwei Ye, Yuanbo Xu, Feiran Huang, Xi Zhang, Philip S. Yu

TL;DR
This survey thoroughly examines hallucinations in large language models, analyzing their causes, detection methods, and solutions to improve reliability and reduce misinformation in critical sectors.
Contribution
It provides a comprehensive categorization and analysis of hallucination causes, detection strategies, and evaluates current solutions to address this challenge in LLMs.
Findings
Identified key causes of hallucinations in LLMs
Evaluated effectiveness of existing detection methods
Proposed directions for developing better solutions
Abstract
Edgar Allan Poe noted, "Truth often lurks in the shadow of error," highlighting the deep complexity intrinsic to the interplay between truth and falsehood, notably under conditions of cognitive and informational asymmetry. This dynamic is strikingly evident in large language models (LLMs). Despite their impressive linguistic generation capabilities, LLMs sometimes produce information that appears factually accurate but is, in reality, fabricated, an issue often referred to as 'hallucinations'. The prevalence of these hallucinations can mislead users, affecting their judgments and decisions. In sectors such as finance, law, and healthcare, such misinformation risks causing substantial economic losses, legal disputes, and health risks, with wide-ranging consequences.In our research, we have methodically categorized, analyzed the causes, detection methods, and solutions related to LLM…
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