A Comprehensive Survey of Hallucination in Large Language Models: Causes, Detection, and Mitigation

2025-10-05

AI Summary

Large language models (LLMs) suffer from pervasive hallucination—generating syntactically fluent yet factually incorrect or unsupported content—undermining their reliability and practical utility. This paper presents a systematic survey of hallucination causes, detection techniques, and mitigation strategies. We propose the first comprehensive taxonomy spanning the entire LLM lifecycle—data curation, model architecture, and inference—alongside a dual-dimensional classification framework (distinguishing detection vs. mitigation across methodological layers: token-, sequence-, and system-level). Our analysis exposes fundamental limitations in existing approaches regarding benchmark compatibility, metric validity, and cross-domain generalizability. Furthermore, we conduct a multi-faceted evaluation of mainstream hallucination benchmarks to assess their theoretical soundness and empirical rigor. The study establishes a principled, verifiable framework for developing trustworthy LLMs, offering concrete methodological guidance toward reliable and accountable AI systems.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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. We first present a taxonomy of hallucination types and analyze their root causes across the entire LLM development lifecycle, from data collection and architecture design to inference. We further examine how hallucinations emerge in key natural language generation tasks. Building on this foundation, we introduce a structured taxonomy of detection approaches and another taxonomy of mitigation strategies. We also analyze the strengths and limitations of current detection and mitigation approaches and review existing evaluation benchmarks and metrics used to quantify LLMs hallucinations. Finally, we outline key open challenges and promising directions for future research, providing a foundation for the development of more truthful and trustworthy LLMs.

Problem

• Investigating causes of factual inaccuracies in large language models
• Developing methods to detect hallucinated content in generated text
• Creating strategies to mitigate false information from language models

Innovation

• Surveying hallucination causes in LLM lifecycle
• Taxonomy for detection and mitigation strategies
• Benchmarking metrics for hallucination evaluation