mlsanityData to 7 Oct 2026

A Survey on Out-of-Distribution Detection in NLP

arXiv 2305.03236 ↗First posted 5 May 2023cs.CL cs.AI cs.LGcited by 31

Not on the map. Why?

Abstract (arXiv)

Out-of-distribution (OOD) detection is essential for the reliable and safe deployment of machine learning systems in the real world. Great progress has been made over the past years. This paper presents the first review of recent advances in OOD detection with a particular focus on natural language processing approaches. First, we provide a formal definition of OOD detection and discuss several related fields. We then categorize recent algorithms into three classes according to the data they used: (1) OOD data available, (2) OOD data unavailable + in-distribution (ID) label available, and (3) OOD data unavailable + ID label unavailable. Third, we introduce datasets, applications, and metrics. Finally, we summarize existing work and present potential future research topics.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). Builds on 96 of its 142 references that are in the map. First posted on arXiv 5 May 2023.

Builds on

96 of 142 references.

References · 96

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  5. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  6. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  7. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  8. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  9. 2022Generative Adversarial Networkscited by 5,603
  10. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  11. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  12. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
38 more
  1. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  2. 2020Supervised Contrastive Learningcited by 2,803
  3. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,362
  4. 2019Language Models as Knowledge Bases?cited by 1,922
  5. 2016Concrete Problems in AI Safetycited by 1,582
  6. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,472
  7. 2020WILDS: A Benchmark of in-the-Wild Distribution Shiftscited by 1,366
  8. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,325
  9. 2020Energy-based Out-of-distribution Detectioncited by 1,296
  10. 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,274
  11. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
  12. 2018Deep Anomaly Detection with Outlier Exposurecited by 939
  13. 2017Selective Classification for Deep Neural Networkscited by 799
  14. 2021Generalized Out-of-Distribution Detection: A Surveycited by 796
  15. 2021Generalizing to Unseen Domains: A Survey on Domain Generalizationcited by 762
  16. 2021Domain Generalization: A Surveycited by 760
  17. 2022Out-of-Distribution Detection with Deep Nearest Neighborscited by 596
  18. 2017Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methodscited by 542
  19. 2015Towards Open Set Deep Networkscited by 533
  20. 2019Using Self-Supervised Learning Can Improve Model Robustness and Uncertaintycited by 511
  21. 2019Likelihood Ratios for Out-of-Distribution Detectioncited by 451
  22. 2020Efficient Intent Detection with Dual Sentence Encoderscited by 419
  23. 2017Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samplescited by 389
  24. 2019An Evaluation Dataset for Intent Classification and Out-of-Scope Predictioncited by 378
  25. 2016SeqGAN: Sequence Generative Adversarial Nets with Policy Gradientcited by 375
  26. 2015Recent Advances in Convolutional Neural Networkscited by 360
  27. 2019Deep Learning for Anomaly Detection: A Surveycited by 357
  28. 2021Open-Set Recognition: a Good Closed-Set Classifier is All You Need?cited by 351
  29. 2022Visual Classification via Description from Large Language Modelscited by 331
  30. 2020Calibration of Pre-trained Transformerscited by 307
  31. 2022VOS: Learning What You Don't Know by Virtual Outlier Synthesiscited by 300
  32. 2020Pretrained Transformers Improve Out-of-Distribution Robustnesscited by 293
  33. 2020Supervised Contrastive Learning for Pre-trained Language Model Fine-tuningcited by 275
  34. 2022OpenOOD: Benchmarking Generalized Out-of-Distribution Detectioncited by 267
  35. 2022Prompting GPT-3 To Be Reliablecited by 258
  36. 2020Selective Question Answering under Domain Shiftcited by 228
  37. 2014Towards Open World Recognitioncited by 226
  38. 2021Internet-Augmented Dialogue Generationcited by 217

50 most cited of 96 All 96 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 7; 2024: 12; 2025: 6; 2026: 6.

31 papers.

Sort:

By area:

  1. 2024ARGS: Alignment as Reward-Guided Searchcited by 99
  2. 2024Process Reward Model with Q-Value Rankingscited by 78
  3. 2024Out-of-Distribution Detection: A Task-Oriented Survey of Recent Advancescited by 24
  4. 2024Embedding Trajectory for Out-of-Distribution Detection in Mathematical Reasoningcited by 18
  5. 2025GOLD: Graph Out-of-Distribution Detection via Implicit Adversarial Latent Generationcited by 15
  6. 2023Exploring Large Language Models for Multi-Modal Out-of-Distribution Detectioncited by 12
  7. 2023Measuring the Robustness of NLP Models to Domain Shiftscited by 11
  8. 2024On the Learnability of Out-of-distribution Detectioncited by 8
  9. 2025Human Texts Are Outliers: Detecting LLM-generated Texts via Out-of-distribution Detectioncited by 8
  10. 2023Long-Tailed Question Answering in an Open Worldcited by 7
  11. 2024Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Surveycited by 6
  12. 2023Domain Incremental Lifelong Learning in an Open Worldcited by 5
19 more
  1. 2023FLatS: Principled Out-of-Distribution Detection with Feature-Based Likelihood Ratio Scorecited by 5
  2. 2023MSAC: Multiple Speech Attribute Control Method for Reliable Speech Emotion Recognitioncited by 4
  3. 2023LUNA: A Model-Based Universal Analysis Framework for Large Language Modelscited by 4
  4. 2024NCDD: Nearest Centroid Distance Deficit for Out-Of-Distribution Detection in Gastrointestinal Visioncited by 3
  5. 2025Text Meets Topology: Rethinking Out-of-distribution Detection in Text-Rich Networkscited by 3
  6. 2024Faithfulness and the Notion of Adversarial Sensitivity in NLP Explanationscited by 2
  7. 2024Towards a Middleware for Large Language Modelscited by 2
  8. 2024FEVER-OOD: Free Energy Vulnerability Elimination for Robust Out-of-Distribution Detectioncited by 2
  9. 2025Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Frameworkcited by 2
  10. 2024Self-supervised New Activity Detection in Sensor-based Smart Environmentscited by 1
  11. 2024Handling Ontology Gaps in Semantic Parsingcited by 1
  12. 2025Out-of-Distribution Detection for Continual Learning: Design Principles and Benchmarkingcited by 1
  13. 2026LLM-Enhanced Energy Contrastive Learning for Out-of-Distribution Detection in Text-Attributed Graphscited by 1
  14. 2026What Information Matters? Graph Out-of-Distribution Detection via Tri-Component Information Decompositioncited by 1
  15. 2025ACE and Diverse Generalization via Selective Disagreementcited by 0
  16. 2026Vision Also You Need: Navigating Out-of-Distribution Detection with Multimodal Large Language Modelcited by 0
  17. 2026To Predict or Not to Predict? Towards reliable uncertainty estimation in the presence of noisecited by 0
  18. 2026Domain Restriction via Multi SAE Layer Transitionscited by 0
  19. 2026Respecting Modality Gap in Post-hoc Out-of-distribution Detection with Pre-trained Vision-Language Modelscited by 0