Data to 7 Oct 2026

Quantifying Uncertainties in Natural Language Processing Tasks

arXiv 1811.07253 ↗First posted 18 Nov 2018cs.CL cs.AI cs.LGcited by 79

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Abstract (arXiv)

Reliable uncertainty quantification is a first step towards building explainable, transparent, and accountable artificial intelligent systems. Recent progress in Bayesian deep learning has made such quantification realizable. In this paper, we propose novel methods to study the benefits of characterizing model and data uncertainties for natural language processing (NLP) tasks. With empirical experiments on sentiment analysis, named entity recognition, and language modeling using convolutional and recurrent neural network models, we show that explicitly modeling uncertainties is not only necessary to measure output confidence levels, but also useful at enhancing model performances in various NLP tasks.

Key facts. Cited by 79 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Nov 2018.

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Citing papers per year: 2021: 13; 2022: 11; 2023: 18; 2024: 19; 2025: 16; 2026: 2.

79 papers.

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2023Robots That Ask For Help: Uncertainty Alignment for Large Language Model Plannerscited by 298
2021On Hallucination and Predictive Uncertainty in Conditional Language Generationcited by 176
2024Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similaritiescited by 156
2024A Survey of Uncertainty Estimation in LLMs: Theory Meets Practicecited by 54
2023Active Instruction Tuning: Improving Cross-Task Generalization by Training on Prompt Sensitive Taskscited by 53
2024Conformal Prediction for Natural Language Processing: A Surveycited by 52
2024Uncertainty Quantification for In-Context Learning of Large Language Modelscited by 45
2024MARS: Meaning-Aware Response Scoring for Uncertainty Estimation in Generative LLMscited by 45
2023Uncertainty in Natural Language Processing: Sources, Quantification, and Applicationscited by 44
2022Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priorscited by 39
2022Weakly Supervised Data Augmentation Through Prompting for Dialogue Understandingcited by 32
2021Distributed NLI: Learning to Predict Human Opinion Distributions for Language Reasoningcited by 31

38 more

2023CLARA: Classifying and Disambiguating User Commands for Reliable Interactive Robotic Agentscited by 28
2025Trust Me, I'm Wrong: LLMs Hallucinate with Certainty Despite Knowing the Answercited by 26
2023On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Studycited by 25
2021A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applicationscited by 24
2021Uncertainty-Aware Reliable Text Classificationcited by 23
2021Uncertainty-Aware Balancing for Multilingual and Multi-Domain Neural Machine Translation Trainingcited by 19
2023Hyperbolic Active Learning for Semantic Segmentation under Domain Shiftcited by 18
2024Probabilistically Correct Language-based Multi-Robot Planning using Conformal Predictioncited by 18
2024Addressing Uncertainty in LLMs to Enhance Reliability in Generative AIcited by 15
2025Herd Behavior: Investigating Peer Influence in LLM-based Multi-Agent Systemscited by 15
2023CAVEN: An Embodied Conversational Agent for Efficient Audio-Visual Navigation in Noisy Environmentscited by 14
2024Uncertainty is Fragile: Manipulating Uncertainty in Large Language Modelscited by 13
2025Uncertainty Quantification and Decomposition for LLM-based Recommendationcited by 12
2022Calibration of Machine Reading Systems at Scalecited by 11
2023Toward Reliable Human Pose Forecasting with Uncertaintycited by 11
2023Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networkscited by 10
2021Trust It or Not: Confidence-Guided Automatic Radiology Report Generationcited by 9
2023Understanding Difficulty-based Sample Weighting with a Universal Difficulty Measurecited by 9
2025Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Taskscited by 9
2021Attribution of Predictive Uncertainties in Classification Modelscited by 8
2023Reliable Gradient-free and Likelihood-free Prompt Tuningcited by 8
2022Efficient, Uncertainty-based Moderation of Neural Networks Text Classifierscited by 7
2022SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parserscited by 7
2023Bag of Tricks for In-Distribution Calibration of Pretrained Transformerscited by 7
2023Uncertainty-Aware Bootstrap Learning for Joint Extraction on Distantly-Supervised Datacited by 7
2024Uncertainty-boosted Robust Video Activity Anticipationcited by 7
2021Giving Commands to a Self-Driving Car: How to Deal with Uncertain Situations?cited by 6
2025The Consistency Hypothesis in Uncertainty Quantification for Large Language Modelscited by 6
2025HACK: Hallucinations Along Certainty and Knowledge Axescited by 6
2021Tracing Halpha Fibrils through Bayesian Deep Learningcited by 5
2021Estimating Predictive Uncertainty Under Program Data Distribution Shiftcited by 5
2024Self-Evaluation of Large Language Model based on Glass-box Featurescited by 5
2024Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuningcited by 5
2021Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forumscited by 4
2021Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimationcited by 4
2022Exploring the Learning Difficulty of Data Theory and Measurecited by 4
2024Taking Action Towards Graceful Interaction: The Effects of Performing Actions on Modelling Policies for Instruction Clarification Requestscited by 4
2025AdaptiveLog: An Adaptive Log Analysis Framework with the Collaboration of Large and Small Language Modelcited by 4

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