mlsanityData to 14 Oct 2026

CoT-UQ: Improving Response-wise Uncertainty Quantification in LLMs with Chain-of-Thought

arXiv 2502.17214 ↗First posted 24 Feb 2025cs.CL cs.LG stat.MLcited by 23

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

Abstract (arXiv)

Large language models (LLMs) excel in many tasks but struggle to accurately quantify uncertainty in their generated responses. This limitation makes it challenging to detect misinformation and ensure reliable decision-making. Existing uncertainty quantification (UQ) methods for LLMs are primarily prompt-wise rather than response-wise, often requiring multiple response samples, which incurs high computational costs. Moreover, LLMs have been shown to be overconfident, particularly when using reasoning steps to derive their answers. In this work, we propose CoT-UQ, a response-wise UQ framework that integrates LLMs' inherent reasoning capabilities through Chain-of-Thought (CoT) into the UQ process. CoT-UQ captures critical information during inference by extracting keywords from each reasoning step and assessing their importance to the final answer. This key reasoning information is then aggregated to produce a final uncertainty estimate. We conduct extensive experiments based on Llama Family with model sizes varying from 8B to 13B across logical and mathematical reasoning tasks. Experimental results demonstrate that CoT-UQ significantly outperforms existing UQ methods, achieving an average improvement of 5.9% AUROC compared to current UQ methods. The code is available at: https://github.com/ZBox1005/CoT-UQ.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning. Builds on 27 of its 34 references that are in the map. First posted on arXiv 24 Feb 2025.

Builds on

27 of 34 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning1 Aug
  2. 2023Large Language Model Guided Tree-of-Thought15 May
  3. 2022Language Models (Mostly) Know What They Know11 Jul

Other references · 24

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  4. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  5. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  6. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  7. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  8. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,282
  9. 2023Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMscited by 974
  10. 2021Are NLP Models really able to Solve Simple Math Word Problems?cited by 878
  11. 2023Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedbackcited by 812
  12. 2022Teaching Models to Express Their Uncertainty in Wordscited by 751
12 more
  1. 2022Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoningcited by 334
  2. 2021A Diverse Corpus for Evaluating and Developing English Math Word Problem Solverscited by 318
  3. 2023Towards Revealing the Mystery behind Chain of Thought: A Theoretical Perspectivecited by 294
  4. 2024LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinationscited by 244
  5. 2023Fine-tuning Language Models for Factualitycited by 221
  6. 2023A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generationcited by 206
  7. 2024Language Model Cascades: Token-level uncertainty and beyondcited by 121
  8. 2024GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluationscited by 120
  9. 2022Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysiscited by 104
  10. 2024MARS: Meaning-Aware Response Scoring for Uncertainty Estimation in Generative LLMscited by 46
  11. 2024ERA-CoT: Improving Chain-of-Thought through Entity Relationship Analysiscited by 18
  12. 2025Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs) More Self-Confident, Especially When They are Wrongcited by 11

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 6; 2026: 17.

23 papers.

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  1. 2025Uncertainty Quantification and Confidence Calibration in Large Language Models: A Surveycited by 95
  2. 2025Position: Uncertainty Quantification Needs Reassessment for Large-language Model Agentscited by 32
  3. 2025TokUR: Token-Level Uncertainty Estimation for Large Language Model Reasoningcited by 24
  4. 2026Confidence Estimation for LLMs in Multi-turn Interactionscited by 12
  5. 2026AgentForesight: Online Auditing for Early Failure Prediction in Multi-Agent Systemscited by 11
  6. 2025Dive into the Agent Matrix: A Realistic Evaluation of Self-Replication Risk in LLM Agentscited by 7
  7. 2025Uncertainty Quantification for Retrieval-Augmented Reasoningcited by 6
  8. 2025Let the Model Distribute Its Doubt: Confidence Estimation through Verbalized Probability Distributioncited by 3
  9. 2026Semantic Calibration Prevails Where Token Confidence Fails: Benchmarking Long-Form Scientific QAcited by 3
  10. 2026SELFDOUBT: Uncertainty Quantification for Reasoning LLMs via the Hedge-to-Verify Ratiocited by 3
  11. 2026Uncertainty Propagation in LLM-Based Systemscited by 3
  12. 2026Large Language Models Decide Early and Explain Latercited by 2
11 more
  1. 2026A Single Layer to Explain Them All:Understanding Massive Activations in Large Language Modelscited by 2
  2. 2026Quantifying Consistency in LLM Logical Reasoning via Structural Uncertaintycited by 2
  3. 2026TableMind++: An Uncertainty-Aware Programmatic Agent for Tool-Augmented Table Reasoningcited by 1
  4. 2026ART: Attention Replacement Technique to Improve Factuality in LLMscited by 1
  5. 2026Monitoring Web Agents Without Internal Signals: Observable Trajectories and Key-Step Supervisioncited by 1
  6. 2026Quantifying and Understanding Uncertainty in Large Reasoning Modelscited by 0
  7. 2026VERDI: Single-Call Confidence Estimation for Verification-Based LLM Judges via Decomposed Inferencecited by 0
  8. 2026GUT: Quantifying and Optimizing the Reasoning Uncertainty of LLMs via Graph Complexitycited by 0
  9. 2026Uncertainty Quantification for LLM Agents: A Taxonomy, an Evaluation Protocol, and an Empirical Studycited by 0
  10. 2026Dream2Discern: Learning from Hard Latent Variants for Runtime Failure Prediction in Multi-Agent Systemscited by 0
  11. 2026Argument Graph Uncertainty: Quantifying Uncertainty from the Logical Structure of Reasoning Chainscited by 0

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  1. 2026ChainUQ: Reasoning Consistency-Aware Uncertainty Quantification for Large Language Models
  2. 2026Uncertainty Quantification for LLM Function-Calling
  3. 2026Embedding Perturbation may Better Reflect Intermediate-Step Uncertainty in LLM Reasoning
  4. 2026Estimating Uncertainty from Reasoning: A Large-Scale Study of Multi- and Crosslingual MCQA Performance in LLMs
  5. 2024MAQA: Evaluating Uncertainty Quantification in LLMs Regarding Data Uncertainty
  6. 2026Probability is Not Enough: Exploring and Counting Divergent Tokens for Reasoning Uncertainty Quantification in LLMs
  7. 2026The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models
  8. 2025Uncertainty-Aware Answer Selection for Improved Reasoning in Multi-LLM Systems