mlsanityData to 7 Oct 2026

Rethinking the Uncertainty: A Critical Review and Analysis in the Era of Large Language Models

arXiv 2410.20199 ↗First posted 26 Oct 2024cs.AIcited by 6

On the map: LLM reasoning → Test-time compute and efficient thinking

Abstract (arXiv)

In recent years, Large Language Models (LLMs) have become fundamental to a broad spectrum of artificial intelligence applications. As the use of LLMs expands, precisely estimating the uncertainty in their predictions has become crucial. Current methods often struggle to accurately identify, measure, and address the true uncertainty, with many focusing primarily on estimating model confidence. This discrepancy is largely due to an incomplete understanding of where, when, and how uncertainties are injected into models. This paper introduces a comprehensive framework specifically designed to identify and understand the types and sources of uncertainty, aligned with the unique characteristics of LLMs. Our framework enhances the understanding of the diverse landscape of uncertainties by systematically categorizing and defining each type, establishing a solid foundation for developing targeted methods that can precisely quantify these uncertainties. We also provide a detailed introduction to key related concepts and examine the limitations of current methods in mission-critical and safety-sensitive applications. The paper concludes with a perspective on future directions aimed at enhancing the reliability and practical adoption of these methods in real-world scenarios.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning. Builds on 47 of its 116 references that are in the map. First posted on arXiv 26 Oct 2024.

Builds on

47 of 116 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Self-Evaluation Guided Beam Search for Reasoning1 May

Other references · 46

  1. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
  2. 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,014
  3. 2023A Survey of Large Language Modelscited by 2,537
  4. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
  5. 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
  6. 2023Inference-Time Intervention: Eliciting Truthful Answers from a Language Modelcited by 1,259
  7. 2021A General Language Assistant as a Laboratory for Alignmentcited by 1,023
  8. 2023Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMscited by 946
  9. 2023Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedbackcited by 788
  10. 2022Teaching Models to Express Their Uncertainty in Wordscited by 731
  11. 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 695
  12. 2021A Survey of Uncertainty in Deep Neural Networkscited by 675
34 more
  1. 2023The Internal State of an LLM Knows When It's Lyingcited by 644
  2. 2020Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stoppingcited by 377
  3. 2018Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problemcited by 323
  4. 2023Generating with Confidence: Uncertainty Quantification for Black-box Large Language Modelscited by 286
  5. 2023Do Large Language Models Know What They Don't Know?cited by 238
  6. 2020Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learningcited by 208
  7. 2022Exploring Length Generalization in Large Language Modelscited by 204
  8. 2024Secrets of RLHF in Large Language Models Part II: Reward Modelingcited by 170
  9. 2024The Effect of Sampling Temperature on Problem Solving in Large Language Modelscited by 168
  10. 2024Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Modelscited by 158
  11. 2021Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Modelscited by 141
  12. 2018Analyzing Uncertainty in Neural Machine Translationcited by 126
  13. 2023Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHFcited by 125
  14. 2022Holistic Evaluation of Language Modelscited by 115
  15. 2023Do Language Models Know When They're Hallucinating References?cited by 115
  16. 2024Relying on the Unreliable: The Impact of Language Models' Reluctance to Express Uncertaintycited by 106
  17. 2024Investigating Data Contamination for Pre-training Language Modelscited by 91
  18. 2023A Survey on Uncertainty Quantification Methods for Deep Learningcited by 78
  19. 2023Calibration in Deep Learning: A Survey of the State-of-the-Artcited by 72
  20. 2024RLHF from Heterogeneous Feedback via Personalization and Preference Aggregationcited by 71
  21. 2023Knowledge of Knowledge: Exploring Known-Unknowns Uncertainty with Large Language Modelscited by 60
  22. 2022A Close Look into the Calibration of Pre-trained Language Modelscited by 57
  23. 2024Towards Robust Alignment of Language Models: Distributionally Robustifying Direct Preference Optimizationcited by 52
  24. 2020Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examplescited by 40
  25. 2024InternalInspector I2: Robust Confidence Estimation in LLMs through Internal Statescited by 29
  26. 2024Large Language Models for Anomaly and Out-of-Distribution Detection: A Surveycited by 24
  27. 2024Direct Preference Optimization With Unobserved Preference Heterogeneity: The Necessity of Ternary Preferencescited by 23
  28. 2024Resilience of Large Language Models for Noisy Instructionscited by 20
  29. 2023How Good Are LLMs at Out-of-Distribution Detection?cited by 15
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  34. 2018A Variational Dirichlet Framework for Out-of-Distribution Detectioncited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 3.

6 papers.

  1. 2025An overview of model uncertainty and variability in LLM-based sentiment analysis. Challenges, mitigation strategies and the role of explainabilitycited by 15
  2. 2026BAS: A Decision-Theoretic Approach to Evaluating Large Language Model Confidencecited by 5
  3. 2025Beyond Correctness: Confidence-Aware Reward Modeling for Enhancing Large Language Model Reasoningcited by 3
  4. 2026VLM-UQBench: A Benchmark for Modality-Specific and Cross-Modality Uncertainties in Vision Language Modelscited by 3
  5. 2025VIDEE: Visual and Interactive Decomposition, Execution, and Evaluation of Text Analytics with Intelligent Agentscited by 0
  6. 2026Position: Uncertainty Quantification in LLMs is Just Unsupervised Clusteringcited by 0

Similar, not cited

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  1. 2025Uncertainty Quantification and Confidence Calibration in Large Language Models: A Survey
  2. 2025Revisiting Uncertainty Estimation and Calibration of Large Language Models
  3. 2026The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models
  4. 2025Can LLMs Detect Their Confabulations? Estimating Reliability in Uncertainty-Aware Language Models
  5. 2025Position: Uncertainty Quantification Needs Reassessment for Large-language Model Agents
  6. 2026Uncertainty Quantification for LLM Agents: A Taxonomy, an Evaluation Protocol, and an Empirical Study
  7. 2026Rethinking Uncertainty Evaluation in Large Language Models
  8. 2026Estimating Uncertainty from Reasoning: A Large-Scale Study of Multi- and Crosslingual MCQA Performance in LLMs