# AI Hallucinations: A Misnomer Worth Clarifying

arXiv: https://arxiv.org/abs/2401.06796 · HTML: https://mlsanity.com/paper/2401.06796/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 87 of its 334 references that are in the map.
- First posted on arXiv 9 Jan 2024.

## Builds on (most cited first, 30 of 87)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [On Faithfulness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2005.00661/) (2020)
- [SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models](https://mlsanity.com/paper/2303.08896/) (2023)
- [Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://mlsanity.com/paper/2309.01219/) (2023)
- [Retrieval Augmentation Reduces Hallucination in Conversation](https://mlsanity.com/paper/2104.07567/) (2021)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models](https://mlsanity.com/paper/2305.11747/) (2023)
- [Six Challenges for Neural Machine Translation](https://mlsanity.com/paper/1706.03872/) (2017)
- [ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge](https://mlsanity.com/paper/2303.14070/) (2023)
- [FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization](https://mlsanity.com/paper/2005.03754/) (2020)
- [ToTTo: A Controlled Table-To-Text Generation Dataset](https://mlsanity.com/paper/2004.14373/) (2020)
- [Mathematical Capabilities of ChatGPT](https://mlsanity.com/paper/2301.13867/) (2023)
- [Challenges in Data-to-Document Generation](https://mlsanity.com/paper/1707.08052/) (2017)
- [Factuality Enhanced Language Models for Open-Ended Text Generation](https://mlsanity.com/paper/2206.04624/) (2022)
- [On Hallucination and Predictive Uncertainty in Conditional Language Generation](https://mlsanity.com/paper/2103.15025/) (2021)
- [A Categorical Archive of ChatGPT Failures](https://mlsanity.com/paper/2302.03494/) (2023)
- [Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text](https://mlsanity.com/paper/2202.06935/) (2022)
- [Detecting Hallucinated Content in Conditional Neural Sequence Generation](https://mlsanity.com/paper/2011.02593/) (2020)
- [Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering](https://mlsanity.com/paper/2307.16877/) (2023)
- [From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy](https://mlsanity.com/paper/2307.00691/) (2023)
- [A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation](https://mlsanity.com/paper/2104.08704/) (2021)
- [On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?](https://mlsanity.com/paper/2204.07931/) (2022)
- [Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization](https://mlsanity.com/paper/2109.09784/) (2021)
- [Generative AI](https://mlsanity.com/paper/2309.07930/) (2023)
- [FaithDial: A Faithful Benchmark for Information-Seeking Dialogue](https://mlsanity.com/paper/2204.10757/) (2022)
- [Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding](https://mlsanity.com/paper/2104.08455/) (2021)
- [WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences](https://mlsanity.com/paper/2306.07906/) (2023)

## Built on by (most cited first, 30 of 34)

- [LLM Post-Training: A Deep Dive into Reasoning Large Language Models](https://mlsanity.com/paper/2502.21321/) (2025)
- [How AI Impacts Skill Formation](https://mlsanity.com/paper/2601.20245/) (2026)
- [CodePori: Large-Scale System for Autonomous Software Development Using Multi-Agent Technology](https://mlsanity.com/paper/2402.01411/) (2024)
- [An Audit on the Perspectives and Challenges of Hallucinations in NLP](https://mlsanity.com/paper/2404.07461/) (2024)
- [ReSeek: A Self-Correcting Framework for Search Agents with Instructive Rewards](https://mlsanity.com/paper/2510.00568/) (2025)
- [Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding](https://mlsanity.com/paper/2510.15253/) (2025)
- [A Review on Generative AI For Text-To-Image and Image-To-Image Generation and Implications To Scientific Images](https://mlsanity.com/paper/2502.21151/) (2025)
- [Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor](https://mlsanity.com/paper/2506.14652/) (2025)
- [Benchmarking Cognitive Domains for LLMs: Insights from Taiwanese Hakka Culture](https://mlsanity.com/paper/2409.01556/) (2024)
- [Iterative Flow Matching: Path Correction and Gradual Refinement for Enhanced Generative Modeling](https://mlsanity.com/paper/2502.16445/) (2025)
- [Solving the Unsolvable: Translating Case Law in Hong Kong](https://mlsanity.com/paper/2501.09444/) (2025)
- [How LLMs Comprehend Temporal Meaning in Narratives: A Case Study in Cognitive Evaluation of LLMs](https://mlsanity.com/paper/2507.14307/) (2025)
- [A Principle-based Framework for the Development and Evaluation of Large Language Models for Health and Wellness](https://mlsanity.com/paper/2512.08936/) (2025)
- [LLM2TEA: An Agentic AI Designer for Discovery with Generative Evolutionary Multitasking](https://mlsanity.com/paper/2406.14917/) (2024)
- [Evaluating the Accuracy of Chatbots in Financial Literature](https://mlsanity.com/paper/2411.07031/) (2024)
- [Artificial Intelligence for Biomedical Video Generation](https://mlsanity.com/paper/2411.07619/) (2024)
- [HIDE and Seek: Detecting Hallucinations in Language Models via Decoupled Representations](https://mlsanity.com/paper/2506.17748/) (2025)
- [MetaRAG: Metamorphic Testing for Hallucination Detection in RAG Systems](https://mlsanity.com/paper/2509.09360/) (2025)
- [Toward Cultural Interpretability: A Linguistic Anthropological Framework for Describing and Evaluating Large Language Models (LLMs)](https://mlsanity.com/paper/2411.05200/) (2024)
- [Towards a rigorous evaluation of RAG systems: the challenge of due diligence](https://mlsanity.com/paper/2507.21753/) (2025)
- [Leveraging NTPs for Efficient Hallucination Detection in VLMs](https://mlsanity.com/paper/2509.20379/) (2025)
- [Strategic Polysemy in AI Discourse: A Philosophical Analysis of Language, Hype, and Power](https://mlsanity.com/paper/2604.21043/) (2026)
- [Do Large Language Models Hallucinate Electric Fata Morganas?](https://mlsanity.com/paper/2608.18816/) (2026)
- [Accelerating db-A* for Kinodynamic Motion Planning Using Diffusion](https://mlsanity.com/paper/2503.05539/) (2025)
- [Learning to Choose or Choosing to Learn: Best-of-N vs. Supervised Fine-Tuning for Bit String Generation](https://mlsanity.com/paper/2505.17288/) (2025)
- [Machine Mirages: Defining the Undefined](https://mlsanity.com/paper/2506.13990/) (2025)
- [Roadmap for using large language models (LLMs) to accelerate cross-disciplinary research with an example from computational biology](https://mlsanity.com/paper/2507.03722/) (2025)
- [Charting the Future of Scholarly Knowledge with AI: A Community Perspective](https://mlsanity.com/paper/2509.02581/) (2025)
- [An Artificial Intelligence Value at Risk Approach: Metrics and Models](https://mlsanity.com/paper/2509.18394/) (2025)
- [Evaluating Cultural Knowledge Processing in Large Language Models: A Cognitive Benchmarking Framework Integrating Retrieval-Augmented Generation](https://mlsanity.com/paper/2511.01649/) (2025)

## Abstract (arXiv)

As large language models continue to advance in Artificial Intelligence (AI), text generation systems have been shown to suffer from a problematic phenomenon termed often as "hallucination." However, with AI's increasing presence across various domains including medicine, concerns have arisen regarding the use of the term itself. In this study, we conducted a systematic review to identify papers defining "AI hallucination" across fourteen databases. We present and analyze definitions obtained across all databases, categorize them based on their applications, and extract key points within each category. Our results highlight a lack of consistency in how the term is used, but also help identify several alternative terms in the literature. We discuss implications of these and call for a more unified effort to bring consistency to an important contemporary AI issue that can affect multiple domains significantly.