mlsanityData to 14 Oct 2026

The Pitfalls of Defining Hallucination

arXiv 2401.07897 ↗First posted 15 Jan 2024cs.CLcited by 5

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Abstract (arXiv)

Despite impressive advances in Natural Language Generation (NLG) and Large Language Models (LLMs), researchers are still unclear about important aspects of NLG evaluation. To substantiate this claim, I examine current classifications of hallucination and omission in Data-text NLG, and I propose a logic-based synthesis of these classfications. I conclude by highlighting some remaining limitations of all current thinking about hallucination and by discussing implications for LLMs.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 15 of its 31 references that are in the map. First posted on arXiv 15 Jan 2024.

Builds on

15 of 31 references.

References · 15

  1. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
  2. 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 900
  3. 2018Object Hallucination in Image Captioningcited by 669
  4. 2017Six Challenges for Neural Machine Translationcited by 335
  5. 2017Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluationcited by 206
  6. 2015A Neural Conversational Modelcited by 196
  7. 2021The Curious Case of Hallucinations in Neural Machine Translationcited by 161
  8. 2023We're Afraid Language Models Aren't Modeling Ambiguitycited by 126
  9. 2022Detecting and Mitigating Hallucinations in Machine Translation: Model Internal Workings Alone Do Well, Sentence Similarity Even Bettercited by 75
  10. 2020Evaluating Semantic Accuracy of Data-to-Text Generation with Natural Language Inferencecited by 43
  11. 2020Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelitycited by 35
  12. 2020A Gold Standard Methodology for Evaluating Accuracy in Data-To-Text Systemscited by 34
3 more
  1. 2022Human Evaluation and Correlation with Automatic Metrics in Consultation Note Generationcited by 33
  2. 2023FuseCap: Leveraging Large Language Models for Enriched Fused Image Captionscited by 33
  3. 2023Evaluating Generative Models for Graph-to-Text Generationcited by 16

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 1; 2026: 1.

5 papers.

  1. 2024Hallucination is Inevitable: An Innate Limitation of Large Language Modelscited by 332
  2. 2024A Survey of Automatic Hallucination Evaluation on Natural Language Generationcited by 7
  3. 2025Place Matters: Comparing LLM Hallucination Rates for Place-Based Legal Queriescited by 3
  4. 2026Detecting Contextual Hallucinations in LLMs with Frequency-Aware Attentioncited by 2
  5. 2024Mitigating Hallucinations Using Ensemble of Knowledge Graph and Vector Store in Large Language Models to Enhance Mental Health Supportcited by 1