mlsanityData to 7 Oct 2026

Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey

arXiv 2409.01980 ↗First posted 3 Sep 2024cs.LGcited by 24

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Abstract (arXiv)

Detecting anomalies or out-of-distribution (OOD) samples is critical for maintaining the reliability and trustworthiness of machine learning systems. Recently, Large Language Models (LLMs) have demonstrated their effectiveness not only in natural language processing but also in broader applications due to their advanced comprehension and generative capabilities. The integration of LLMs into anomaly and OOD detection marks a significant shift from the traditional paradigm in the field. This survey focuses on the problem of anomaly and OOD detection under the context of LLMs. We propose a new taxonomy to categorize existing approaches into two classes based on the role played by LLMs. Following our proposed taxonomy, we further discuss the related work under each of the categories and finally discuss potential challenges and directions for future research in this field. We also provide an up-to-date reading list of relevant papers.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 61 of its 114 references that are in the map. First posted on arXiv 3 Sep 2024.

Builds on

61 of 114 references.

References · 61

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  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2023GPT-4 Technical Reportcited by 18,132
  5. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
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  7. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  8. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
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  12. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
38 more
  1. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,362
  2. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,539
  3. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,472
  4. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,325
  5. 2020Energy-based Out-of-distribution Detectioncited by 1,296
  6. 2019Semantic Image Synthesis with Spatially-Adaptive Normalizationcited by 1,295
  7. 2018A Survey Of Methods For Explaining Black Box Modelscited by 970
  8. 2016Pixel Recurrent Neural Networkscited by 814
  9. 2021Generalized Out-of-Distribution Detection: A Surveycited by 796
  10. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  11. 2022Out-of-Distribution Detection with Deep Nearest Neighborscited by 596
  12. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 591
  13. 2018Real-world Anomaly Detection in Surveillance Videoscited by 570
  14. 2023VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Taskscited by 499
  15. 2021ReAct: Out-of-distribution Detection With Rectified Activationscited by 486
  16. 2019Scaling Out-of-Distribution Detection for Real-World Settingscited by 465
  17. 2020CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instancescited by 441
  18. 2021Exploring the Limits of Out-of-Distribution Detectioncited by 283
  19. 2023AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detectioncited by 270
  20. 2020Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervisioncited by 235
  21. 2018Learning from positive and unlabeled data: a surveycited by 218
  22. 2022Delving into Out-of-Distribution Detection with Vision-Language Representationscited by 215
  23. 2018Adversarially Learned One-Class Classifier for Novelty Detectioncited by 202
  24. 2021A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challengescited by 154
  25. 2021Zero-Shot Out-of-Distribution Detection Based on the Pre-trained Model CLIPcited by 151
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  27. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 147
  28. 2023CLIPN for Zero-Shot OOD Detection: Teaching CLIP to Say Nocited by 130
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  33. 2024Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLMcited by 63
  34. 2023Large Language Models are Diverse Role-Players for Summarization Evaluationcited by 62
  35. 2023CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detectioncited by 58
  36. 2024AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2cited by 53
  37. 2024FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localizationcited by 51
  38. 2023Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Leadcited by 40

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 9; 2026: 11.

24 papers.

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  1. 2024AD-LLM: Benchmarking Large Language Models for Anomaly Detectioncited by 25
  2. 2024Out-of-Distribution Detection: A Task-Oriented Survey of Recent Advancescited by 24
  3. 2025BadThink: Triggered Overthinking Attacks on Chain-of-Thought Reasoning in Large Language Modelscited by 7
  4. 2024Rethinking the Uncertainty: A Critical Review and Analysis in the Era of Large Language Modelscited by 6
  5. 2024CMOOD: Concept-based Multi-label OOD Detectioncited by 6
  6. 2025Out-of-Distribution Detection using Synthetic Data Generationcited by 4
  7. 2025Foundation Models for Anomaly Detection: Vision and Challengescited by 3
  8. 2025Text Meets Topology: Rethinking Out-of-distribution Detection in Text-Rich Networkscited by 3
  9. 2025VLMDiff: Leveraging Vision-Language Models for Multi-Class Anomaly Detection with Diffusioncited by 3
  10. 2026CoAct: Co-Active LLM Preference Learning with Human-AI Synergycited by 3
  11. 2025Out-of-Distribution Detection for Safety Assurance of AI and Autonomous Systemscited by 2
  12. 2025VADER: Towards Causal Video Anomaly Understanding with Relation-Aware Large Language Modelscited by 2
12 more
  1. 2026A Principled Approach to Unsupervised Anomaly Detectioncited by 2
  2. 2026Breaking Semantic Hegemony: Decoupling Principal and Residual Subspaces for Generalized OOD Detectioncited by 1
  3. 2026Managing Uncertainty in LLM-based Multi-Agent System Operationcited by 1
  4. 2026LLM-Enhanced Energy Contrastive Learning for Out-of-Distribution Detection in Text-Attributed Graphscited by 1
  5. 2025Adaptive and Robust Data Poisoning Detection and Sanitization in Wearable IoT Systems using Large Language Modelscited by 0
  6. 2025JT-DA: Enhancing Data Analysis with Tool-Integrated Table Reasoning Large Language Modelscited by 0
  7. 2026From Exposure to Internalization: Dual-Stream Calibration for In-context Clinical Reasoningcited by 0
  8. 2026Node-to-Neighborhood Semantic Consistency: Text-Topology Alignment for TAGs Anomaly Detectioncited by 0
  9. 2026Influence of Prompt Engineering on Small Language Models for Guarded Query Routingcited by 0
  10. 2026SABRE: A Multi-Agent Approach for Selecting Out-of-Distribution Detectors Under a Budgetcited by 0
  11. 2026On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Modelscited by 0
  12. 2026The Privacy Fallacy of Crowdsourced Fine-Tuning: Extracting Proprietary Data via Topic-Based Poisoningcited by 0