mlsanityData to 7 Oct 2026

Uncertainty in Natural Language Processing: Sources, Quantification, and Applications

arXiv 2306.04459 ↗First posted 5 Jun 2023cs.CLcited by 44

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Abstract (arXiv)

As a main field of artificial intelligence, natural language processing (NLP) has achieved remarkable success via deep neural networks. Plenty of NLP tasks have been addressed in a unified manner, with various tasks being associated with each other through sharing the same paradigm. However, neural networks are black boxes and rely on probability computation. Making mistakes is inevitable. Therefore, estimating the reliability and trustworthiness (in other words, uncertainty) of neural networks becomes a key research direction, which plays a crucial role in reducing models' risks and making better decisions. Therefore, in this survey, we provide a comprehensive review of uncertainty-relevant works in the NLP field. Considering the data and paradigms characteristics, we first categorize the sources of uncertainty in natural language into three types, including input, system, and output. Then, we systemically review uncertainty quantification approaches and the main applications. Finally, we discuss the challenges of uncertainty estimation in NLP and discuss potential future directions, taking into account recent trends in the field. Though there have been a few surveys about uncertainty estimation, our work is the first to review uncertainty from the NLP perspective.

Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). Builds on 96 of its 156 references that are in the map. First posted on arXiv 5 Jun 2023.

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96 of 156 references.

References · 96

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 20; 2025: 14; 2026: 8.

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  1. 2024Benchmarking LLMs via Uncertainty Quantificationcited by 124
  2. 2025Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous Concept Spacecited by 109
  3. 2023Copy Suppression: Comprehensively Understanding an Attention Headcited by 73
  4. 2023Uncertainty in Natural Language Generation: From Theory to Applicationscited by 61
  5. 2024Conformal Prediction for Natural Language Processing: A Surveycited by 52
  6. 2024Internal Consistency and Self-Feedback in Large Language Models: A Surveycited by 49
  7. 2024Think Twice Before Trusting: Self-Detection for Large Language Models through Comprehensive Answer Reflectioncited by 40
  8. 2024Knowledge Boundary of Large Language Models: A Surveycited by 36
  9. 2024Calibrating Long-form Generations from Large Language Modelscited by 31
  10. 2024InternalInspector I2: Robust Confidence Estimation in LLMs through Internal Statescited by 29
  11. 2025Trust Me, I'm Wrong: LLMs Hallucinate with Certainty Despite Knowing the Answercited by 26
  12. 2024Reference-free Hallucination Detection for Large Vision-Language Modelscited by 23
32 more
  1. 2024Atomic Calibration of LLMs in Long-Form Generationscited by 23
  2. 2024Black-box Uncertainty Quantification Method for LLM-as-a-Judgecited by 18
  3. 2024Probabilities of Chat LLMs Are Miscalibrated but Still Predict Correctness on Multiple-Choice Q&Acited by 16
  4. 2025Revisiting Epistemic Markers in Confidence Estimation: Can Markers Accurately Reflect Large Language Models' Uncertainty?cited by 16
  5. 2025Device-Cloud Collaborative LLM Inference with Multi-Modal, Multi-Task, Multi-Turn Conversationscited by 13
  6. 2025Uncertainty Distillation: Teaching Language Models to Express Semantic Confidencecited by 13
  7. 2026Knowing When to Abstain: Medical LLMs Under Clinical Uncertaintycited by 13
  8. 2024LinkNER: Linking Local Named Entity Recognition Models to Large Language Models using Uncertaintycited by 12
  9. 2024Uncertainty Quantification in Large Language Models Through Convex Hull Analysiscited by 12
  10. 2025HACK: Hallucinations Along Certainty and Knowledge Axescited by 6
  11. 2025COPU: Conformal Prediction for Uncertainty Quantification in Natural Language Generationcited by 5
  12. 2025UNCERTAINTY-LINE: Length-Invariant Estimation of Uncertainty for Large Language Modelscited by 5
  13. 2026Walking Through Uncertainty: An Empirical Study of Uncertainty Estimation for Audio-Aware Large Language Modelscited by 4
  14. 2024Semantic Density: Uncertainty Quantification for Large Language Models through Confidence Measurement in Semantic Spacecited by 3
  15. 2024Survey and Taxonomy: The Role of Data-Centric AI in Transformer-Based Time Series Forecastingcited by 3
  16. 2024Question Rephrasing for Quantifying Uncertainty in Large Language Models: Applications in Molecular Chemistry Taskscited by 3
  17. 2024Uncertainty-Aware Mean Opinion Score Predictioncited by 3
  18. 2025Embracing Diversity: A Multi-Perspective Approach with Soft Labelscited by 3
  19. 2025Challenges in Understanding Modality Conflict in Vision-Language Modelscited by 3
  20. 2025Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for LLMscited by 2
  21. 2024NALA: an Effective and Interpretable Entity Alignment Methodcited by 1
  22. 2025Your Model is Overconfident, and Other Lies We Tell Ourselvescited by 1
  23. 2025Self-signals Driven Multi-LLM Debate for Efficient and Accurate Reasoningcited by 1
  24. 2026When Should a Language Model Trust Itself? Same-Model Self-Verification as a Conditional Confidence Signalcited by 1
  25. 2026Diagnosing LLM Arbitration Behavior over Pre-evidence Epistemic States in RAG-based Fact-Checkingcited by 1
  26. 2024Domain-specific or Uncertainty-aware models: Does it really make a difference for biomedical text classification?cited by 0
  27. 2024KBM: Delineating Knowledge Boundary for Adaptive Retrieval in Large Language Modelscited by 0
  28. 2025Using Large Language Models for Legal Decision-Making in Austrian Value-Added Tax Law: A Comparative Studycited by 0
  29. 2026Uncertainty Quantification for Named Entity Recognition via Full-Sequence and Subsequence Conformal Predictioncited by 0
  30. 2026RealBirdID: Benchmarking Bird Species Identification in the Era of MLLMscited by 0
  31. 2026Why Don't You Know? Evaluating the Impact of Uncertainty Sources on Uncertainty Quantification in LLMscited by 0
  32. 2026Characterizing Overconfident Failure in LLM-Based Code Generationcited by 0