# A comprehensive taxonomy of hallucinations in Large Language Models

arXiv: https://arxiv.org/abs/2508.01781 · HTML: https://mlsanity.com/paper/2508.01781/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2025.

## Built on by (most cited first, 18 of 18)

- [The Landscape of Agentic Reinforcement Learning for LLMs: A Survey](https://mlsanity.com/paper/2509.02547/) (2025)
- [Large Language Models Hallucination: A Comprehensive Survey](https://mlsanity.com/paper/2510.06265/) (2025)
- [When to Trust the Answer: Question-Aligned Semantic Nearest Neighbor Entropy for Safer Surgical VQA](https://mlsanity.com/paper/2511.01458/) (2025)
- [Prompt-Response Semantic Divergence Metrics for Faithfulness Hallucination and Misalignment Detection in Large Language Models](https://mlsanity.com/paper/2508.10192/) (2025)
- [Hallucination is Inevitable for LLMs with the Open World Assumption](https://mlsanity.com/paper/2510.05116/) (2025)
- [Topic Identification in LLM Input-Output Pairs through the Lens of Information Bottleneck](https://mlsanity.com/paper/2509.03533/) (2025)
- [Making LLMs Reliable When It Matters Most: A Five-Layer Architecture for High-Stakes Decisions](https://mlsanity.com/paper/2511.07669/) (2025)
- [UCPO: Uncertainty-Aware Policy Optimization](https://mlsanity.com/paper/2601.22648/) (2026)
- [Rethinking Agentic Reinforcement Learning In Large Language Models](https://mlsanity.com/paper/2604.27859/) (2026)
- [Divergent Recommendations, Convergent Diagnoses: Cross-Provider Failure-Mode Convergence in AI Commercial Recommendation](https://mlsanity.com/paper/2606.26116/) (2026)
- [RAG-BioQA: A Retrieval-Augmented Generation Framework for Long-Form Biomedical Question Answering](https://mlsanity.com/paper/2510.01612/) (2025)
- [When Guessing is Rewarded: Rethinking Language Model Evaluation with Distributional Uncertainty Scoring](https://mlsanity.com/paper/2510.04302/) (2025)
- [Evaluating Metrics for Safety with LLM-as-Judges](https://mlsanity.com/paper/2512.15617/) (2025)
- [Text-Graph Synergy: A Bidirectional Verification and Completion Framework for RAG](https://mlsanity.com/paper/2605.05643/) (2026)
- [Hypergraph Enterprise Agentic Reasoner over Heterogeneous Business Systems](https://mlsanity.com/paper/2605.14259/) (2026)
- [A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models](https://mlsanity.com/paper/2606.26879/) (2026)
- [DEEPO: Dual-Entropy Enhanced Policy Optimization for Hallucination in MLLMs](https://mlsanity.com/paper/2609.28570/) (2026)
- [RAIM: Robust Aggregation of Inexpensive Models for Hallucination Detection](https://mlsanity.com/paper/2609.39229/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have revolutionized natural language processing, yet their propensity for hallucination, generating plausible but factually incorrect or fabricated content, remains a critical challenge. This report provides a comprehensive taxonomy of LLM hallucinations, beginning with a formal definition and a theoretical framework that posits its inherent inevitability in computable LLMs, irrespective of architecture or training. It explores core distinctions, differentiating between intrinsic (contradicting input context) and extrinsic (inconsistent with training data or reality), as well as factuality (absolute correctness) and faithfulness (adherence to input). The report then details specific manifestations, including factual errors, contextual and logical inconsistencies, temporal disorientation, ethical violations, and task-specific hallucinations across domains like code generation and multimodal applications. It analyzes the underlying causes, categorizing them into data-related issues, model-related factors, and prompt-related influences. Furthermore, the report examines cognitive and human factors influencing hallucination perception, surveys evaluation benchmarks and metrics for detection, and outlines architectural and systemic mitigation strategies. Finally, it introduces web-based resources for monitoring LLM releases and performance. This report underscores the complex, multifaceted nature of LLM hallucinations and emphasizes that, given their theoretical inevitability, future efforts must focus on robust detection, mitigation, and continuous human oversight for responsible and reliable deployment in critical applications.