mlsanityData to 14 Oct 2026

Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness

arXiv 2308.16175 ↗First posted 30 Aug 2023cs.CL cs.AIcited by 122

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Abstract (arXiv)

We introduce BSDetector, a method for detecting bad and speculative answers from a pretrained Large Language Model by estimating a numeric confidence score for any output it generated. Our uncertainty quantification technique works for any LLM accessible only via a black-box API, whose training data remains unknown. By expending a bit of extra computation, users of any LLM API can now get the same response as they would ordinarily, as well as a confidence estimate that cautions when not to trust this response. Experiments on both closed and open-form Question-Answer benchmarks reveal that BSDetector more accurately identifies incorrect LLM responses than alternative uncertainty estimation procedures (for both GPT-3 and ChatGPT). By sampling multiple responses from the LLM and considering the one with the highest confidence score, we can additionally obtain more accurate responses from the same LLM, without any extra training steps. In applications involving automated evaluation with LLMs, accounting for our confidence scores leads to more reliable evaluation in both human-in-the-loop and fully-automated settings (across both GPT 3.5 and 4).

Key facts. Cited by 122 papers in the map (data through 14 Oct 2026). Builds on 33 of its 41 references that are in the map. First posted on arXiv 30 Aug 2023.

Builds on

33 of 41 references.

References · 33

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  4. 2017On Calibration of Modern Neural Networkscited by 4,960
  5. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  6. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  7. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  8. 2020Learning to summarize from human feedbackcited by 2,588
  9. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
  10. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,181
  11. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  12. 2022Language Models (Mostly) Know What They Knowcited by 1,856
21 more
  1. 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,822
  2. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,328
  3. 2023WizardLM: Empowering large pre-trained language models to follow complex instructionscited by 1,125
  4. 2021Are NLP Models really able to Solve Simple Math Word Problems?cited by 878
  5. 2023Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generationcited by 819
  6. 2023Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedbackcited by 812
  7. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 810
  8. 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 792
  9. 2022Teaching Models to Express Their Uncertainty in Wordscited by 751
  10. 2023Instruction Tuning with GPT-4cited by 686
  11. 2015Weight Uncertainty in Neural Networkscited by 554
  12. 2018Accurate Uncertainties for Deep Learning Using Calibrated Regressioncited by 422
  13. 2023Generating with Confidence: Uncertainty Quantification for Black-box Large Language Modelscited by 293
  14. 2018Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learningcited by 207
  15. 2018Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Samplingcited by 169
  16. 2015A Theoretically Grounded Application of Dropout in Recurrent Neural Networkscited by 161
  17. 2023InstructZero: Efficient Instruction Optimization for Black-Box Large Language Modelscited by 68
  18. 2023How Many Demonstrations Do You Need for In-context Learning?cited by 52
  19. 2017Bayesian Recurrent Neural Networkscited by 39
  20. 2023When do you need Chain-of-Thought Prompting for ChatGPT?cited by 35
  21. 2019Maximizing Overall Diversity for Improved Uncertainty Estimates in Deep Ensemblescited by 30

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 37; 2025: 48; 2026: 34.

122 papers.

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  1. 2024Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMscited by 259
  2. 2023A Survey of Confidence Estimation and Calibration in Large Language Modelscited by 256
  3. 2025Scalable Best-of-N Selection for Large Language Models via Self-Certaintycited by 187
  4. 2025Uncertainty Quantification and Confidence Calibration in Large Language Models: A Surveycited by 95
  5. 2024Non-Determinism of "Deterministic" LLM Settingscited by 92
  6. 2024On Verbalized Confidence Scores for LLMscited by 83
  7. 2024LUQ: Long-text Uncertainty Quantification for LLMscited by 81
  8. 2025SAFE: Multitask Failure Detection for Vision-Language-Action Modelscited by 80
  9. 2024Towards Conversational Diagnostic AIcited by 79
  10. 2025A Survey of Uncertainty Estimation Methods on Large Language Modelscited by 62
  11. 2024A Survey of Uncertainty Estimation in LLMs: Theory Meets Practicecited by 55
  12. 2024Internal Consistency and Self-Feedback in Large Language Models: A Surveycited by 50
38 more
  1. 2024MARS: Meaning-Aware Response Scoring for Uncertainty Estimation in Generative LLMscited by 46
  2. 2024Knowledge Boundary of Large Language Models: A Surveycited by 40
  3. 2024Uncertainty quantification in fine-tuned LLMs using LoRA ensemblescited by 37
  4. 2024Calibrating Long-form Generations from Large Language Modelscited by 32
  5. 2024Content Moderation by LLM: From Accuracy to Legitimacycited by 30
  6. 2024Improving Uncertainty Quantification in Large Language Models via Semantic Embeddingscited by 30
  7. 2025Uncertainty Quantification for LLMs through Minimum Bayes Risk: Bridging Confidence and Consistencycited by 30
  8. 2024Cycles of Thought: Measuring LLM Confidence through Stable Explanationscited by 29
  9. 2024Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Modelscited by 29
  10. 2025Understanding the Uncertainty of LLM Explanations: A Perspective Based on Reasoning Topologycited by 25
  11. 2024Do Not Design, Learn: A Trainable Scoring Function for Uncertainty Estimation in Generative LLMscited by 23
  12. 2024LLM Uncertainty Quantification through Directional Entailment Graph and Claim Level Response Augmentationcited by 23
  13. 2026ExpSeek: Self-Triggered Experience Seeking for Web Agentscited by 23
  14. 2025From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centeredcited by 21
  15. 2024Estimating the Hallucination Rate of Generative AIcited by 20
  16. 2024Fairness and Bias in Multimodal AI: A Surveycited by 20
  17. 2024Rethinking Uncertainty Estimation in LLMs: A Principled Single-Sequence Measurecited by 18
  18. 2025Uncertainty Quantification of Large Language Models through Multi-Dimensional Responsescited by 18
  19. 2025Uncertainty Quantification for Language Models: A Suite of Black-Box, White-Box, LLM Judge, and Ensemble Scorerscited by 18
  20. 2025CER: Confidence Enhanced Reasoning in LLMscited by 17
  21. 2025MUR: Momentum Uncertainty guided Reasoning for Large Language Modelscited by 17
  22. 2023Uncertainty-aware Language Modeling for Selective Question Answeringcited by 16
  23. 2025Revisiting Epistemic Markers in Confidence Estimation: Can Markers Accurately Reflect Large Language Models' Uncertainty?cited by 16
  24. 2025MetaFaith: Faithful Natural Language Uncertainty Expression in LLMscited by 16
  25. 2024Prompt Stability Scoring for Text Annotation with Large Language Modelscited by 15
  26. 2024Regurgitative Training: The Value of Real Data in Training Large Language Modelscited by 15
  27. 2024Perceptions of Linguistic Uncertainty by Language Models and Humanscited by 15
  28. 2024SECURE: Benchmarking Large Language Models for Cybersecuritycited by 14
  29. 2025Is LLM an Overconfident Judge? Unveiling the Capabilities of LLMs in Detecting Offensive Language with Annotation Disagreementcited by 14
  30. 2025VeriLA: A Human-Centered Evaluation Framework for Interpretable Verification of LLM Agent Failurescited by 14
  31. 2025Language Model Uncertainty Quantification with Attention Chaincited by 14
  32. 2025Conformal Language Model Reasoning with Coherent Factualitycited by 14
  33. 2024Uncertainty is Fragile: Manipulating Uncertainty in Large Language Modelscited by 13
  34. 2025Enhancing LLM Reliability via Explicit Knowledge Boundary Modelingcited by 13
  35. 2024On the Reliability of Large Language Models for Causal Discoverycited by 12
  36. 2025Uncertainty Quantification for Hallucination Detection in Large Language Models: Foundations, Methodology, and Future Directionscited by 11
  37. 2023AutoHall: Automated Factuality Hallucination Dataset Generation for Large Language Modelscited by 10
  38. 2024Graph-based Confidence Calibration for Large Language Modelscited by 10

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