mlsanityData to 7 Oct 2026

Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling

arXiv 2311.08718 ↗First posted 15 Nov 2023cs.CLcited by 113

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Abstract (arXiv)

Uncertainty decomposition refers to the task of decomposing the total uncertainty of a predictive model into aleatoric (data) uncertainty, resulting from inherent randomness in the data-generating process, and epistemic (model) uncertainty, resulting from missing information in the model's training data. In large language models (LLMs) specifically, identifying sources of uncertainty is an important step toward improving reliability, trustworthiness, and interpretability, but remains an important open research question. In this paper, we introduce an uncertainty decomposition framework for LLMs, called input clarification ensembling, which can be applied to any pre-trained LLM. Our approach generates a set of clarifications for the input, feeds them into an LLM, and ensembles the corresponding predictions. We show that, when aleatoric uncertainty arises from ambiguity or under-specification in LLM inputs, this approach makes it possible to factor an (unclarified) LLM's predictions into separate aleatoric and epistemic terms, using a decomposition similar to the one employed by Bayesian neural networks. Empirical evaluations demonstrate that input clarification ensembling provides accurate and reliable uncertainty quantification on several language processing tasks. Code and data are available at https://github.com/UCSB-NLP-Chang/llm_uncertainty.

Key facts. Cited by 113 papers in the map (data through 7 Oct 2026). Builds on 44 of its 66 references that are in the map. First posted on arXiv 15 Nov 2023.

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44 of 66 references.

References · 44

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  1. 2022Teaching Models to Express Their Uncertainty in Wordscited by 731
  2. 2022Large Language Models Can Self-Improvecited by 656
  3. 2018Predictive Uncertainty Estimation via Prior Networkscited by 555
  4. 2019A Simple Baseline for Bayesian Uncertainty in Deep Learningcited by 555
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Citing papers per year: 2024: 29; 2025: 49; 2026: 35.

113 papers.

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  1. 2024To Believe or Not to Believe Your LLMcited by 120
  2. 2024Know Your Limits: A Survey of Abstention in Large Language Modelscited by 116
  3. 2024A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directionscited by 115
  4. 2025Maximizing Confidence Alone Improves Reasoningcited by 97
  5. 2024Uncertainty Estimation and Quantification for LLMs: A Simple Supervised Approachcited by 73
  6. 2024Learning to Route LLMs with Confidence Tokenscited by 66
  7. 2024Calibrating Large Language Models Using Their Generations Onlycited by 63
  8. 2025Reasoning Models Better Express Their Confidencecited by 63
  9. 2024Confidence Regulation Neurons in Language Modelscited by 57
  10. 2025A Survey of Uncertainty Estimation Methods on Large Language Modelscited by 57
  11. 2024A Survey of Uncertainty Estimation in LLMs: Theory Meets Practicecited by 54
  12. 2024Distinguishing the Knowable from the Unknowable with Language Modelscited by 45
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  4. 2024Improving Uncertainty Estimation through Semantically Diverse Language Generationcited by 34
  5. 2024Don't Just Say "I don't know"! Self-aligning Large Language Models for Responding to Unknown Questions with Explanationscited by 30
  6. 2024MAQA: Evaluating Uncertainty Quantification in LLMs Regarding Data Uncertaintycited by 25
  7. 2025TokUR: Token-Level Uncertainty Estimation for Large Language Model Reasoningcited by 24
  8. 2025Prototypical Human-AI Collaboration Behaviors from LLM-Assisted Writing in the Wildcited by 24
  9. 2024Multilingual Blending: LLM Safety Alignment Evaluation with Language Mixturecited by 22
  10. 2024BIRD: A Trustworthy Bayesian Inference Framework for Large Language Modelscited by 18
  11. 2025MUR: Momentum Uncertainty guided Reasoning for Large Language Modelscited by 17
  12. 2024Contextualized Sequence Likelihood: Enhanced Confidence Scores for Natural Language Generationcited by 16
  13. 2024Perceptions of Linguistic Uncertainty by Language Models and Humanscited by 15
  14. 2024Hallucination Detection in LLMs: Fast and Memory-Efficient Fine-Tuned Modelscited by 15
  15. 2025MetaFaith: Faithful Natural Language Uncertainty Expression in LLMscited by 15
  16. 2025The Illusion of Certainty: Uncertainty Quantification for LLMs Fails under Ambiguitycited by 15
  17. 2026Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunitiescited by 15
  18. 2026Ask or Assume? Uncertainty-Aware Clarification-Seeking in Coding Agentscited by 15
  19. 2024A Novel Characterization of the Population Area Under the Risk Coverage Curve (AURC) and Rates of Finite Sample Estimatorscited by 13
  20. 2025Enhancing Hallucination Detection through Noise Injectioncited by 13
  21. 2025Language Model Uncertainty Quantification with Attention Chaincited by 13
  22. 2025TruthPrInt: Mitigating Large Vision-Language Models Object Hallucination Via Latent Truthful-Guided Pre-Interventioncited by 11
  23. 2025Uncertainty Quantification for Machine Learning in Healthcare: A Surveycited by 11
  24. 2025Do not Abstain! Identify and Solve the Uncertaintycited by 11
  25. 2024Neural Topic Modeling with Large Language Models in the Loopcited by 10
  26. 2025Always Tell Me The Odds: Fine-grained Conditional Probability Estimationcited by 9
  27. 2025Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Taskscited by 9
  28. 2025Mapping from Meaning: Addressing the Miscalibration of Prompt-Sensitive Language Modelscited by 9
  29. 2024In-Context Learning May Not Elicit Trustworthy Reasoning: A-Not-B Errors in Pretrained Language Modelscited by 8
  30. 2025Uncertainty Quantification in Retrieval Augmented Question Answeringcited by 8
  31. 2025Exploring the Potential for Large Language Models to Demonstrate Rational Probabilistic Beliefscited by 8
  32. 2025Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systemscited by 8
  33. 2025Fine-Grained Uncertainty Decomposition in Large Language Models: A Spectral Approachcited by 8
  34. 2025MAP: Multi-user Personalization with Collaborative LLM-powered Agentscited by 7
  35. 2025Adaptive Elicitation of Latent Information Using Natural Languagecited by 7
  36. 2025Conformal Information Pursuit for Interactively Guiding Large Language Modelscited by 7
  37. 2024Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMscited by 5
  38. 2025Uncertainty Unveiled: Can Exposure to More In-context Examples Mitigate Uncertainty for Large Language Models?cited by 5

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