# Large Language Models Hallucination: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2510.06265 · HTML: https://mlsanity.com/paper/2510.06265/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- Builds on 209 of its 275 references that are in the map.
- First posted on arXiv 5 Oct 2025.

## Builds on (most cited first, 30 of 209)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Towards Understanding Sycophancy in Language Models](https://mlsanity.com/paper/2310.13548/) (2023)
- [Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs](https://mlsanity.com/paper/2306.13063/) (2023)

## Built on by (most cited first, 30 of 49)

- [Failure Modes in LLM Systems: A System-Level Taxonomy for Reliable AI Applications](https://mlsanity.com/paper/2511.19933/) (2025)
- [Past, Present, and Future of Bug Tracking in the Generative AI Era](https://mlsanity.com/paper/2510.08005/) (2025)
- [Stop Rewarding Hallucinated Steps: Faithfulness-Aware Step-Level Reinforcement Learning for Small Reasoning Models](https://mlsanity.com/paper/2602.05897/) (2026)
- [Are LLMs Reliable Code Reviewers? Systematic Overcorrection in Requirement Conformance Judgement](https://mlsanity.com/paper/2603.00539/) (2026)
- [PRISM: Probing Reasoning, Instruction, and Source Memory in LLM Hallucinations](https://mlsanity.com/paper/2604.16909/) (2026)
- [LiteGUI: Distilling Compact GUI Agents with Reinforcement Learning](https://mlsanity.com/paper/2605.07505/) (2026)
- [LegalCiteBench: Evaluating Citation Reliability in Legal Language Models](https://mlsanity.com/paper/2605.10186/) (2026)
- [HalluScore: Large Language Model Hallucination Question Answering Benchmark](https://mlsanity.com/paper/2605.17007/) (2026)
- [CrossHallu: Do Hallucination Signals Generalize Across Languages and Domains in Large Language Model's Internals?](https://mlsanity.com/paper/2607.04029/) (2026)
- [HalluTruthQA: A Fine-Grained Benchmark for Hallucination Detection, Localization, and Explanation in Arabic Question Answering](https://mlsanity.com/paper/2607.20219/) (2026)
- [Planner-Auditor Twin: Agentic Discharge Planning with FHIR-Based LLM Planning, Guideline Recall, Optional Caching and Self-Improvement](https://mlsanity.com/paper/2601.21113/) (2026)
- [A Geometric Taxonomy of Hallucinations in LLMs](https://mlsanity.com/paper/2602.13224/) (2026)
- [On Integrating Resilience and Human Oversight into LLM-Assisted Modeling Workflows for Digital Twins](https://mlsanity.com/paper/2603.25898/) (2026)
- [Plausible but Wrong: A case study on Agentic Failures in Astrophysical Workflows](https://mlsanity.com/paper/2604.25345/) (2026)
- [Useful for Exploration, Risky for Precision: Evaluating AI Tools in Academic Research](https://mlsanity.com/paper/2605.10125/) (2026)
- [From Architecture to Output: Structural Origins of Hallucination in Large Language Models and the Amplifying Role of Data](https://mlsanity.com/paper/2606.07537/) (2026)
- [Masked Diffusion Language Models are Strong and Steerable Text-Based World Models for Agentic RL](https://mlsanity.com/paper/2607.16204/) (2026)
- [Temperature-driven inversion and nonlinear dynamics in ChatGPT-like AIs](https://mlsanity.com/paper/2608.00939/) (2026)
- [HalluTruthQA-4K: A Fine-Grained Corpus and Annotation Process for Arabic Hallucination Detection and Truth Verification](https://mlsanity.com/paper/2608.03966/) (2026)
- [Mitigating Fabrication in Multi-Stage LLM Pipelines for Hiring: An Empirical Evaluation of Prompt Guardrails and Human-in-the-Loop Checkpoints](https://mlsanity.com/paper/2608.26171/) (2026)
- [Toward Epistemic Stability: Engineering Consistent Procedures for Industrial LLM Hallucination Reduction](https://mlsanity.com/paper/2603.10047/) (2026)
- [Distilling Reasoning Without Knowledge: A Framework for Reliable LLMs](https://mlsanity.com/paper/2603.14458/) (2026)
- [Mitigating LLM Hallucinations through Domain-Grounded Tiered Retrieval](https://mlsanity.com/paper/2603.17872/) (2026)
- [DepthCharge: A Domain-Agnostic Framework for Measuring Depth-Dependent Knowledge in Large Language Models](https://mlsanity.com/paper/2603.23514/) (2026)
- [Disentangling Prompt Element Level Risk Factors for Hallucinations and Omissions in Mental Health LLM Responses](https://mlsanity.com/paper/2604.00014/) (2026)
- [Attention Sinks as Internal Signals for Hallucination Detection in Large Language Models](https://mlsanity.com/paper/2604.10697/) (2026)
- [SensingAgents: A Multi-Agent Collaborative Framework for Robust IMU Activity Recognition](https://mlsanity.com/paper/2605.04608/) (2026)
- [Hypergraph Enterprise Agentic Reasoner over Heterogeneous Business Systems](https://mlsanity.com/paper/2605.14259/) (2026)
- [Human-AI Collaboration for Estimating Scientific Replicability](https://mlsanity.com/paper/2605.27394/) (2026)
- [Can LLM Teams Play What? Where? When?](https://mlsanity.com/paper/2605.30459/) (2026)

## Abstract (arXiv)

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.