mlsanityData to 7 Oct 2026

Out-of-Distribution Detection and Selective Generation for Conditional Language Models

arXiv 2209.15558 ↗First posted 30 Sep 2022cs.CLcited by 193

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Abstract (arXiv)

Machine learning algorithms typically assume independent and identically distributed samples in training and at test time. Much work has shown that high-performing ML classifiers can degrade significantly and provide overly-confident, wrong classification predictions, particularly for out-of-distribution (OOD) inputs. Conditional language models (CLMs) are predominantly trained to classify the next token in an output sequence, and may suffer even worse degradation on OOD inputs as the prediction is done auto-regressively over many steps. Furthermore, the space of potential low-quality outputs is larger as arbitrary text can be generated and it is important to know when to trust the generated output. We present a highly accurate and lightweight OOD detection method for CLMs, and demonstrate its effectiveness on abstractive summarization and translation. We also show how our method can be used under the common and realistic setting of distribution shift for selective generation (analogous to selective prediction for classification) of high-quality outputs, while automatically abstaining from low-quality ones, enabling safer deployment of generative language models.

Key facts. Cited by 193 papers in the map (data through 7 Oct 2026). Builds on 49 of its 67 references that are in the map. First posted on arXiv 30 Sep 2022.

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49 of 67 references.

References · 49

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  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
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  5. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
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  12. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,325
37 more
  1. 2018Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarizationcited by 1,308
  2. 2020Energy-based Out-of-distribution Detectioncited by 1,296
  3. 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,281
  4. 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,274
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  6. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
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  11. 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 895
  12. 2017Selective Classification for Deep Neural Networkscited by 799
  13. 2014Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Imagescited by 774
  14. 2022Out-of-Distribution Detection with Deep Nearest Neighborscited by 596
  15. 2019SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarizationcited by 530
  16. 2019Scaling Out-of-Distribution Detection for Real-World Settingscited by 465
  17. 2019Likelihood Ratios for Out-of-Distribution Detectioncited by 451
  18. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 423
  19. 2020Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awarenesscited by 389
  20. 2018Generating Wikipedia by Summarizing Long Sequencescited by 333
  21. 2020Pretrained Transformers Improve Out-of-Distribution Robustnesscited by 293
  22. 2018Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategiescited by 292
  23. 2021A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detectioncited by 206
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  25. 2020Contrastive Training for Improved Out-of-Distribution Detectioncited by 186
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  28. 2019Assessing The Factual Accuracy of Generated Textcited by 103
  29. 2018Abstractive Summarization of Reddit Posts with Multi-level Memory Networkscited by 102
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  31. 2021Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Taskscited by 100
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  33. 2021Types of Out-of-Distribution Texts and How to Detect Themcited by 80
  34. 2021Contrastive Out-of-Distribution Detection for Pretrained Transformerscited by 70
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 23; 2024: 45; 2025: 70; 2026: 54.

193 papers.

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  1. 2023Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMscited by 946
  2. 2024INSIDE: LLMs' Internal States Retain the Power of Hallucination Detectioncited by 317
  3. 2023Generating with Confidence: Uncertainty Quantification for Black-box Large Language Modelscited by 286
  4. 2023A Survey of Confidence Estimation and Calibration in Large Language Modelscited by 253
  5. 2024Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantificationcited by 159
  6. 2023LM-Polygraph: Uncertainty Estimation for Language Modelscited by 130
  7. 2024Language Model Cascades: Token-level uncertainty and beyondcited by 115
  8. 2024Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraphcited by 105
  9. 2024ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretabilitycited by 100
  10. 2024HaloScope: Harnessing Unlabeled LLM Generations for Hallucination Detectioncited by 96
  11. 2025Uncertainty Quantification and Confidence Calibration in Large Language Models: A Surveycited by 93
  12. 2023A Survey on Uncertainty Quantification Methods for Deep Learningcited by 78
38 more
  1. 2023Dream the Impossible: Outlier Imagination with Diffusion Modelscited by 77
  2. 2023Self-Evaluation Improves Selective Generation in Large Language Modelscited by 74
  3. 2024Safeguarding Large Language Models: A Surveycited by 73
  4. 2023Do Androids Know They're Only Dreaming of Electric Sheep?cited by 67
  5. 2024Are Your LLMs Capable of Stable Reasoning?cited by 65
  6. 2023Selectively Answering Ambiguous Questionscited by 63
  7. 2025Sampling-Efficient Test-Time Scaling: Self-Estimating the Best-of-N Sampling in Early Decodingcited by 62
  8. 2023Uncertainty in Natural Language Generation: From Theory to Applicationscited by 61
  9. 2025A Survey of Uncertainty Estimation Methods on Large Language Modelscited by 57
  10. 2024Latent Space Chain-of-Embedding Enables Output-free LLM Self-Evaluationcited by 56
  11. 2024A Survey of Uncertainty Estimation in LLMs: Theory Meets Practicecited by 54
  12. 2025SETS: Leveraging Self-Verification and Self-Correction for Improved Test-Time Scalingcited by 54
  13. 2023Adaptation with Self-Evaluation to Improve Selective Prediction in LLMscited by 49
  14. 2025Steer LLM Latents for Hallucination Detectioncited by 49
  15. 2023Fusing Models with Complementary Expertisecited by 45
  16. 2024Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimensioncited by 45
  17. 2023A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Modelscited by 39
  18. 2024How Does Unlabeled Data Provably Help Out-of-Distribution Detection?cited by 36
  19. 2024Uncertainty quantification in fine-tuned LLMs using LoRA ensemblescited by 35
  20. 2024A Stitch in Time Saves Nine: Small VLM is a Precise Guidance for Accelerating Large VLMscited by 35
  21. 2025Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Homecited by 33
  22. 2025Detection and Mitigation of Hallucination in Large Reasoning Models: A Mechanistic Perspectivecited by 32
  23. 2023A Survey on Out-of-Distribution Detection in NLPcited by 31
  24. 2024On the Challenges and Opportunities in Generative AIcited by 31
  25. 2025ICR Probe: Tracking Hidden State Dynamics for Reliable Hallucination Detection in LLMscited by 31
  26. 2024Characterizing LLM Abstention Behavior in Science QA with Context Perturbationscited by 26
  27. 2023On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Studycited by 25
  28. 2023DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalizationcited by 24
  29. 2025Dynamic Parametric Retrieval Augmented Generation for Test-time Knowledge Enhancementcited by 24
  30. 2025TokUR: Token-Level Uncertainty Estimation for Large Language Model Reasoningcited by 24
  31. 2025The Illusion of Progress: Re-evaluating Hallucination Detection in LLMscited by 22
  32. 2024A Survey on the Honesty of Large Language Modelscited by 21
  33. 2025A Head to Predict and a Head to Question: Pre-trained Uncertainty Quantification Heads for Hallucination Detection in LLM Outputscited by 20
  34. 2025Reliable Text-to-SQL with Adaptive Abstentioncited by 19
  35. 2025Robust Hallucination Detection in LLMs via Adaptive Token Selectioncited by 19
  36. 2024Embedding Trajectory for Out-of-Distribution Detection in Mathematical Reasoningcited by 18
  37. 2024Accounting for Sycophancy in Language Model Uncertainty Estimationcited by 18
  38. 2025Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Modelscited by 18

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