mlsanityData to 7 Oct 2026

A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions

arXiv 2412.05563 ↗First posted 7 Dec 2024cs.CL cs.AIcited by 115

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Abstract (arXiv)

The remarkable performance of large language models (LLMs) in content generation, coding, and common-sense reasoning has spurred widespread integration into many facets of society. However, integration of LLMs raises valid questions on their reliability and trustworthiness, given their propensity to generate hallucinations: plausible, factually-incorrect responses, which are expressed with striking confidence. Previous work has shown that hallucinations and other non-factual responses generated by LLMs can be detected by examining the uncertainty of the LLM in its response to the pertinent prompt, driving significant research efforts devoted to quantifying the uncertainty of LLMs. This survey seeks to provide an extensive review of existing uncertainty quantification methods for LLMs, identifying their salient features, along with their strengths and weaknesses. We present existing methods within a relevant taxonomy, unifying ostensibly disparate methods to aid understanding of the state of the art. Furthermore, we highlight applications of uncertainty quantification methods for LLMs, spanning chatbot and textual applications to embodied artificial intelligence applications in robotics. We conclude with open research challenges in uncertainty quantification of LLMs, seeking to motivate future research.

Key facts. Cited by 115 papers in the map (data through 7 Oct 2026). Builds on 173 of its 244 references that are in the map. First posted on arXiv 7 Dec 2024.

Builds on

173 of 244 references.

References · 173

  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. 2023GPT-4 Technical Reportcited by 18,132
  5. 2024The Llama 3 Herd of Modelscited by 14,192
  6. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  7. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  8. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  9. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  10. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  11. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  12. 2020Measuring Massive Multitask Language Understandingcited by 7,768
38 more
  1. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  2. 2017On Calibration of Modern Neural Networkscited by 4,860
  3. 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,581
  4. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  5. 2019HellaSwag: Can a Machine Really Finish Your Sentence?cited by 4,018
  6. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  7. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  8. 2023Mistral 7Bcited by 3,065
  9. 2023RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Controlcited by 2,993
  10. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
  11. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
  12. 2024OpenVLA: An Open-Source Vision-Language-Action Modelcited by 2,777
  13. 2023GPQA: A Graduate-Level Google-Proof Q&A Benchmarkcited by 2,748
  14. 2017A Broad-Coverage Challenge Corpus for Sentence Understanding through Inferencecited by 2,665
  15. 2022Do As I Can, Not As I Say: Grounding Language in Robotic Affordancescited by 2,302
  16. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,157
  17. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  18. 2022RT-1: Robotics Transformer for Real-World Control at Scalecited by 2,007
  19. 2022Language Models (Mostly) Know What They Knowcited by 1,783
  20. 2021Calibrate Before Use: Improving Few-Shot Performance of Language Modelscited by 1,371
  21. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,362
  22. 2023Sparse Autoencoders Find Highly Interpretable Features in Language Modelscited by 1,359
  23. 2020Transformer Feed-Forward Layers Are Key-Value Memoriescited by 1,307
  24. 2023Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMscited by 946
  25. 2022Toy Models of Superpositioncited by 928
  26. 2021Measuring Coding Challenge Competence With APPScited by 879
  27. 2017RACE: Large-scale ReAding Comprehension Dataset From Examinationscited by 851
  28. 2021Probing Classifiers: Promises, Shortcomings, and Advancescited by 831
  29. 2023Progress measures for grokking via mechanistic interpretabilitycited by 822
  30. 2023Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generationcited by 795
  31. 2023Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedbackcited by 788
  32. 2021Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategiescited by 736
  33. 2022Discovering Latent Knowledge in Language Models Without Supervisioncited by 733
  34. 2022Teaching Models to Express Their Uncertainty in Wordscited by 731
  35. 2021BARTScore: Evaluating Generated Text as Text Generationcited by 719
  36. 2018CoQA: A Conversational Question Answering Challengecited by 674
  37. 2020Language-agnostic BERT Sentence Embeddingcited by 621
  38. 2019Measuring Calibration in Deep Learningcited by 443

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 48; 2026: 66.

115 papers.

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  1. 2025Uncertainty Quantification and Confidence Calibration in Large Language Models: A Surveycited by 93
  2. 2025SAFE: Multitask Failure Detection for Vision-Language-Action Modelscited by 74
  3. 2025No Answer Needed: Predicting LLM Answer Accuracy from Question-Only Linear Probescited by 35
  4. 2025LLM-Powered AI Agent Systems and Their Applications in Industrycited by 22
  5. 2025Uncertainty-Aware Step-wise Verification with Generative Reward Modelscited by 21
  6. 2025From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centeredcited by 21
  7. 2025Agentic AI Needs a Systems Theorycited by 19
  8. 2025Failure-Driven Workflow Refinementcited by 19
  9. 2025Evaluating Uncertainty and Quality of Vision-Language-Action-enabled Robotscited by 17
  10. 2025Uncertainty Quantification for Language Models: A Suite of Black-Box, White-Box, LLM Judge, and Ensemble Scorerscited by 16
  11. 2025World Models That Know When They Don't Know - Controllable Video Generation with Calibrated Uncertaintycited by 16
  12. 2025An overview of model uncertainty and variability in LLM-based sentiment analysis. Challenges, mitigation strategies and the role of explainabilitycited by 15
38 more
  1. 2025Anthropomimetic Uncertainty: What Verbalized Uncertainty in Language Models is Missingcited by 14
  2. 2025Calibrating Verbalized Confidence with Self-Generated Distractorscited by 14
  3. 2025Utility-Diversity Aware Online Batch Selection for LLM Supervised Fine-tuningcited by 14
  4. 2025Query-Level Uncertainty in Large Language Modelscited by 12
  5. 2025Check Yourself Before You Wreck Yourself: Selectively Quitting Improves LLM Agent Safetycited by 12
  6. 2024Rethinking LLM Uncertainty: A Multi-Agent Approach to Estimating Black-Box Model Uncertaintycited by 11
  7. 2025Benchmarking and Confidence Evaluation of LALMs For Temporal Reasoningcited by 10
  8. 2025C-LoRA: Contextual Low-Rank Adaptation for Uncertainty Estimation in Large Language Modelscited by 9
  9. 2025UQLM: A Python Package for Uncertainty Quantification in Large Language Modelscited by 9
  10. 2025Do Large Language Models Know What They Are Capable Of?cited by 9
  11. 2026SELAUR: Self Evolving LLM Agent via Uncertainty-aware Rewardscited by 9
  12. 2026Complementing Self-Consistency with Cross-Model Disagreement for Uncertainty Quantificationcited by 9
  13. 2025Uncertainty Quantification in Retrieval Augmented Question Answeringcited by 8
  14. 2025Societal Alignment Frameworks Can Improve LLM Alignmentcited by 8
  15. 2025Calibrating Uncertainty Quantification of Multi-Modal LLMs using Groundingcited by 8
  16. 2025Orchestrating Human-AI Teams: The Manager Agent as a Unifying Research Challengecited by 8
  17. 2025Position: Towards a Responsible LLM-empowered Multi-Agent Systemscited by 7
  18. 2025Stop Reducing Responsibility in LLM-Powered Multi-Agent Systems to Local Alignmentcited by 7
  19. 2025How Confident are Video Models? Empowering Video Models to Express their Uncertaintycited by 6
  20. 2025Domain-Shift-Aware Conformal Prediction for Large Language Modelscited by 6
  21. 2026SWAY: A Counterfactual Computational Linguistic Approach to Measuring and Mitigating Sycophancycited by 6
  22. 2026LLMs as Signal Detectors: Sensitivity, Bias, and the Temperature-Criterion Analogycited by 5
  23. 2026ProcCtrlBench: Evaluating Process-Level Defects and Control Preservation in LLM Coding Agentscited by 5
  24. 2025When an LLM is apprehensive about its answers -- and when its uncertainty is justifiedcited by 4
  25. 2025Uncertainty-o: One Model-agnostic Framework for Unveiling Uncertainty in Large Multimodal Modelscited by 4
  26. 2025Uncovering the Fragility of Trustworthy LLMs through Chinese Textual Ambiguitycited by 4
  27. 2025LEC: Linear Expectation Constraints for Selection-Conditioned Risk Control in Selective Prediction and Routing Systemscited by 4
  28. 2026LLM Performance Predictors: Learning When to Escalate in Hybrid Human-AI Moderation Systemscited by 4
  29. 2026Recurrent Confidence Chain: Temporal-Aware Uncertainty Quantification in Large Language Modelscited by 4
  30. 2026Fine-Grained Uncertainty Quantification for Long-Form Language Model Outputs: A Comparative Studycited by 4
  31. 2026Verbalizing LLM's Higher-order Uncertainty via Imprecise Probabilitiescited by 4
  32. 2026Cross-Model Disagreement as a Label-Free Correctness Signalcited by 4
  33. 2026Uncertainty Quantification for Flow-Based Vision-Language-Action Modelscited by 4
  34. 2025Comparing Uncertainty Measurement and Mitigation Methods for Large Language Models: A Systematic Reviewcited by 3
  35. 2025Certainty-Guided Reasoning in Large Language Models: A Dynamic Thinking Budget Approachcited by 3
  36. 2025INSIGHT: INference-time Sequence Introspection for Generating Help Triggers in Vision-Language-Action Modelscited by 3
  37. 2025When to Trust the Answer: Question-Aligned Semantic Nearest Neighbor Entropy for Safer Surgical VQAcited by 3
  38. 2026Hallucination Detection and Mitigation in Large Language Modelscited by 3

50 most cited of 115 All 115 (JSON)