mlsanityData to 14 Oct 2026

Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models

arXiv 2307.01379 ↗First posted 3 Jul 2023cs.CL cs.AI cs.LGcited by 150

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Cited by 24 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Large Language Models (LLMs) show promising results in language generation and instruction following but frequently "hallucinate", making their outputs less reliable. Despite Uncertainty Quantification's (UQ) potential solutions, implementing it accurately within LLMs is challenging. Our research introduces a simple heuristic: not all tokens in auto-regressive LLM text equally represent the underlying meaning, as "linguistic redundancy" often allows a few keywords to convey the essence of long sentences. However, current methods underestimate this inequality when assessing uncertainty, causing tokens with limited semantics to be equally or excessively weighted in UQ. To correct this, we propose Shifting Attention to more Relevant (SAR) components at both token- and sentence-levels for better UQ. We conduct extensive experiments involving a range of popular "off-the-shelf" LLMs, such as Vicuna, WizardLM, and LLaMA-2-chat, with model sizes extending up to 33B parameters. We evaluate various free-form question-answering tasks, encompassing domains such as reading comprehension, science Q&A, and medical Q&A. Our experimental results, coupled with a comprehensive demographic analysis, demonstrate the superior performance of SAR. The code is available at https://github.com/jinhaoduan/SAR.

Key facts. Cited by 150 papers in the map (data through 14 Oct 2026). Builds on 30 of its 46 references that are in the map. First posted on arXiv 3 Jul 2023.

Builds on

30 of 46 references.

References · 30

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  4. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  5. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  6. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  7. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  8. 2017Deep reinforcement learning from human preferencescited by 3,912
  9. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
  10. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  11. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
  12. 2022Language Models (Mostly) Know What They Knowcited by 1,856
18 more
  1. 2020What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Examscited by 1,356
  2. 2023WizardLM: Empowering large pre-trained language models to follow complex instructionscited by 1,125
  3. 2017Crowdsourcing Multiple Choice Science Questionscited by 765
  4. 2022Teaching Models to Express Their Uncertainty in Wordscited by 751
  5. 2018CoQA: A Conversational Question Answering Challengecited by 683
  6. 2023Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoningcited by 391
  7. 2022Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoningcited by 334
  8. 2023Large Language Model Guided Tree-of-Thoughtcited by 221
  9. 2020Unsupervised Quality Estimation for Neural Machine Translationcited by 211
  10. 2018Analyzing Uncertainty in Neural Machine Translationcited by 126
  11. 2021DEUP: Direct Epistemic Uncertainty Predictioncited by 104
  12. 2022Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysiscited by 104
  13. 2023TrueTeacher: Learning Factual Consistency Evaluation with Large Language Modelscited by 83
  14. 2021Uncertainty-Aware Machine Translation Evaluationcited by 35
  15. 2020Regression Prior Networkscited by 28
  16. 2022Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcitycited by 25
  17. 2023Towards Healthy AI: Large Language Models Need Therapists Toocited by 13
  18. 2023Uncertainty-Aware Natural Language Inference with Stochastic Weight Averagingcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 19; 2025: 62; 2026: 69.

150 papers.

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  1. 2024Know Your Limits: A Survey of Abstention in Large Language Modelscited by 121
  2. 2024GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluationscited by 120
  3. 2024Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraphcited by 105
  4. 2025Uncertainty Quantification and Confidence Calibration in Large Language Models: A Surveycited by 95
  5. 2024Learning to Route LLMs with Confidence Tokenscited by 67
  6. 2025Rewarding Doubt: A Reinforcement Learning Approach to Calibrated Confidence Expression of Large Language Modelscited by 52
  7. 2025Calibrating Verbal Uncertainty as a Linear Feature to Reduce Hallucinationscited by 48
  8. 2025UProp: Investigating the Uncertainty Propagation of LLMs in Multi-Step Agentic Decision-Makingcited by 42
  9. 2025Parameters vs. Context: Fine-Grained Control of Knowledge Reliance in Language Modelscited by 39
  10. 2024Enhancing Descriptive Image Quality Assessment with A Large-scale Multi-modal Datasetcited by 38
  11. 2025Uncertainty Quantification for LLMs through Minimum Bayes Risk: Bridging Confidence and Consistencycited by 30
  12. 2024Cycles of Thought: Measuring LLM Confidence through Stable Explanationscited by 29
38 more
  1. 2025Estimating LLM Uncertainty with Evidencecited by 26
  2. 2025Understanding the Uncertainty of LLM Explanations: A Perspective Based on Reasoning Topologycited by 25
  3. 2025TokUR: Token-Level Uncertainty Estimation for Large Language Model Reasoningcited by 24
  4. 2024A Survey on the Honesty of Large Language Modelscited by 23
  5. 2025Jailbreak-AudioBench: In-Depth Evaluation and Analysis of Jailbreak Threats for Large Audio Language Modelscited by 23
  6. 2025From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centeredcited by 21
  7. 2025An Empirical Analysis of Uncertainty in Large Language Model Evaluationscited by 20
  8. 2026Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunitiescited by 20
  9. 2024Sample then Identify: A General Framework for Risk Control and Assessment in Multimodal Large Language Modelscited by 19
  10. 2025Robust Hallucination Detection in LLMs via Adaptive Token Selectioncited by 19
  11. 2024Rethinking Uncertainty Estimation in LLMs: A Principled Single-Sequence Measurecited by 18
  12. 2025Verify when Uncertain: Beyond Self-Consistency in Black Box Hallucination Detectioncited by 18
  13. 2025Semantic Energy: Detecting LLM Hallucination Beyond Entropycited by 18
  14. 2024LRP4RAG: Detecting Hallucinations in Retrieval-Augmented Generation via Layer-wise Relevance Propagationcited by 17
  15. 2024UncertaintyRAG: Span-Level Uncertainty Enhanced Long-Context Modeling for Retrieval-Augmented Generationcited by 16
  16. 2025Efficient Hallucination Detection for LLMs Using Uncertainty-Aware Attention Headscited by 16
  17. 2025Revisiting Epistemic Markers in Confidence Estimation: Can Markers Accurately Reflect Large Language Models' Uncertainty?cited by 16
  18. 2024Perceptions of Linguistic Uncertainty by Language Models and Humanscited by 15
  19. 2025Language Model Uncertainty Quantification with Attention Chaincited by 14
  20. 2025Query-Level Uncertainty in Large Language Modelscited by 14
  21. 2025Anthropomimetic Uncertainty: What Verbalized Uncertainty in Language Models is Missingcited by 14
  22. 2025HalluEntity: Benchmarking and Understanding Entity-Level Hallucination Detectioncited by 13
  23. 2024CALMM-Drive: Confidence-Aware Autonomous Driving with Large Multimodal Modelcited by 12
  24. 2025Confidence Calibration in Vision-Language-Action Modelscited by 12
  25. 2025TruthPrInt: Mitigating Large Vision-Language Models Object Hallucination Via Latent Truthful-Guided Pre-Interventioncited by 11
  26. 2025MMBoundary: Advancing MLLM Knowledge Boundary Awareness through Reasoning Step Confidence Calibrationcited by 11
  27. 2025Uncertainty Quantification for Hallucination Detection in Large Language Models: Foundations, Methodology, and Future Directionscited by 11
  28. 2025Learned Hallucination Detection in Black-Box LLMs using Token-level Entropy Production Ratecited by 10
  29. 2024Integrative Decoding: Improve Factuality via Implicit Self-consistencycited by 8
  30. 2025Attention Mechanisms Perspective: Exploring LLM Processing of Graph-Structured Datacited by 8
  31. 2024FactTest: Factuality Testing in Large Language Models with Finite-Sample and Distribution-Free Guaranteescited by 7
  32. 2024Epistemic Integrity in Large Language Modelscited by 7
  33. 2024Not Just Text: Uncovering Vision Modality Typographic Threats in Image Generation Modelscited by 7
  34. 2025Adaptive Elicitation of Latent Information Using Natural Languagecited by 7
  35. 2025Addressing Pitfalls in the Evaluation of Uncertainty Estimation Methods for Natural Language Generationcited by 7
  36. 2025Uncertainty as Feature Gaps: Epistemic Uncertainty Quantification of LLMs in Contextual Question-Answeringcited by 7
  37. 2025TROLL: Trust Regions improve Reinforcement Learning for Large Language Modelscited by 7
  38. 2025Stop Reducing Responsibility in LLM-Powered Multi-Agent Systems to Local Alignmentcited by 7

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