mlsanityData to 7 Oct 2026

Insights into Classifying and Mitigating LLMs' Hallucinations

arXiv 2311.08117 ↗First posted 14 Nov 2023cs.CLcited by 6

Not on the map. Why?

Abstract (arXiv)

The widespread adoption of large language models (LLMs) across diverse AI applications is proof of the outstanding achievements obtained in several tasks, such as text mining, text generation, and question answering. However, LLMs are not exempt from drawbacks. One of the most concerning aspects regards the emerging problematic phenomena known as "Hallucinations". They manifest in text generation systems, particularly in question-answering systems reliant on LLMs, potentially resulting in false or misleading information propagation. This paper delves into the underlying causes of AI hallucination and elucidates its significance in artificial intelligence. In particular, Hallucination classification is tackled over several tasks (Machine Translation, Question and Answer, Dialog Systems, Summarisation Systems, Knowledge Graph with LLMs, and Visual Question Answer). Additionally, we explore potential strategies to mitigate hallucinations, aiming to enhance the overall reliability of LLMs. Our research addresses this critical issue within the HeReFaNMi (Health-Related Fake News Mitigation) project, generously supported by NGI Search, dedicated to combating Health-Related Fake News dissemination on the Internet. This endeavour represents a concerted effort to safeguard the integrity of information dissemination in an age of evolving AI technologies.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 50 of its 58 references that are in the map. First posted on arXiv 14 Nov 2023.

Builds on

50 of 58 references.

References · 50

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  4. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  5. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  6. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  7. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
  8. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
  9. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
  10. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  11. 2022Language Models (Mostly) Know What They Knowcited by 1,783
  12. 2023WizardLM: Empowering large pre-trained language models to follow complex instructionscited by 1,116
38 more
  1. 2022GLM-130B: An Open Bilingual Pre-trained Modelcited by 837
  2. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 787
  3. 2021BARTScore: Evaluating Generated Text as Text Generationcited by 719
  4. 2023The Internal State of an LLM Knows When It's Lyingcited by 644
  5. 2023GPTScore: Evaluate as You Desirecited by 357
  6. 2023How Good Are GPT Models at Machine Translation? A Comprehensive Evaluationcited by 344
  7. 2023Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategiescited by 236
  8. 2023A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generationcited by 202
  9. 2021The Curious Case of Hallucinations in Neural Machine Translationcited by 161
  10. 2023Med-HALT: Medical Domain Hallucination Test for Large Language Modelscited by 156
  11. 2023Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answeringcited by 150
  12. 2019Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Modelscited by 141
  13. 2022On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?cited by 137
  14. 2021Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarizationcited by 128
  15. 2023Hallucinations in Large Multilingual Translation Modelscited by 126
  16. 2018The Fact Extraction and VERification (FEVER) Shared Taskcited by 108
  17. 2022FaithDial: A Faithful Benchmark for Information-Seeking Dialoguecited by 108
  18. 2022Evaluating the Factual Consistency of Large Language Models Through News Summarizationcited by 90
  19. 2023KoLA: Carefully Benchmarking World Knowledge of Large Language Modelscited by 85
  20. 2021Evaluating Attribution in Dialogue Systems: The BEGIN Benchmarkcited by 75
  21. 2021Let there be a clock on the beach: Reducing Object Hallucination in Image Captioningcited by 74
  22. 2023Investigating the Translation Performance of a Large Multilingual Language Model: the Case of BLOOMcited by 58
  23. 2023Detecting and Mitigating Hallucinations in Multilingual Summarisationcited by 48
  24. 2023Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Textcited by 48
  25. 2022Contrastive Learning Reduces Hallucination in Conversationscited by 39
  26. 2022An Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP Taskscited by 36
  27. 2023HalOmi: A Manually Annotated Benchmark for Multilingual Hallucination and Omission Detection in Machine Translationcited by 35
  28. 2023Trapping LLM Hallucinations Using Tagged Context Promptscited by 34
  29. 2023Evaluation of GPT-3.5 and GPT-4 for supporting real-world information needs in healthcare deliverycited by 30
  30. 2022SKILL: Structured Knowledge Infusion for Large Language Modelscited by 27
  31. 2023mmT5: Modular Multilingual Pre-Training Solves Source Language Hallucinationscited by 24
  32. 2023Zero-Resource Hallucination Prevention for Large Language Modelscited by 19
  33. 2023Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbotscited by 18
  34. 2023"Why is this misleading?": Detecting News Headline Hallucinations with Explanationscited by 17
  35. 2023Evaluating Generative Models for Graph-to-Text Generationcited by 16
  36. 2023Simple Token-Level Confidence Improves Caption Correctnesscited by 11
  37. 2023Album Storytelling with Iterative Story-aware Captioning and Large Language Modelscited by 7
  38. 2023Fact-Checking of AI-Generated Reportscited by 6

Built on by

Cited by year

Citing papers per year: 2024: 1; 2025: 5.

6 papers.

  1. 2025LLM Post-Training: A Deep Dive into Reasoning Large Language Modelscited by 129
  2. 2024"Not Aligned" is Not "Malicious": Being Careful about Hallucinations of Large Language Models' Jailbreakcited by 23
  3. 2025HACK: Hallucinations Along Certainty and Knowledge Axescited by 6
  4. 2025Schema-Guided Scene-Graph Reasoning based on Multi-Agent Large Language Model Systemcited by 5
  5. 2025U2F: Encouraging SWE-Agent to Seize Novelty without Losing Feasibilitycited by 1
  6. 2025Learning to Choose or Choosing to Learn: Best-of-N vs. Supervised Fine-Tuning for Bit String Generationcited by 0