mlsanityData to 14 Oct 2026

Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus

arXiv 2311.13230 ↗First posted 22 Nov 2023cs.CL cs.AIcited by 99

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Abstract (arXiv)

Large Language Models (LLMs) have gained significant popularity for their impressive performance across diverse fields. However, LLMs are prone to hallucinate untruthful or nonsensical outputs that fail to meet user expectations in many real-world applications. Existing works for detecting hallucinations in LLMs either rely on external knowledge for reference retrieval or require sampling multiple responses from the LLM for consistency verification, making these methods costly and inefficient. In this paper, we propose a novel reference-free, uncertainty-based method for detecting hallucinations in LLMs. Our approach imitates human focus in factuality checking from three aspects: 1) focus on the most informative and important keywords in the given text; 2) focus on the unreliable tokens in historical context which may lead to a cascade of hallucinations; and 3) focus on the token properties such as token type and token frequency. Experimental results on relevant datasets demonstrate the effectiveness of our proposed method, which achieves state-of-the-art performance across all the evaluation metrics and eliminates the need for additional information.

Key facts. Cited by 99 papers in the map (data through 14 Oct 2026). Builds on 35 of its 63 references that are in the map. First posted on arXiv 22 Nov 2023.

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35 of 63 references.

References · 35

  1. 2017Attention Is All You Needcited by 60,361
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  5. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
  6. 2018Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarizationcited by 1,317
  7. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,282
  8. 2023BloombergGPT: A Large Language Model for Financecited by 939
  9. 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivitycited by 894
  10. 2015Scheduled Sampling for Sequence Prediction with Recurrent Neural Networkscited by 803
  11. 2022GPT-NeoX-20B: An Open-Source Autoregressive Language Modelcited by 713
  12. 2020SummEval: Re-evaluating Summarization Evaluationcited by 674
23 more
  1. 2019Evaluating the Factual Consistency of Abstractive Text Summarizationcited by 584
  2. 2021SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarizationcited by 407
  3. 2021Entity-Based Knowledge Conflicts in Question Answeringcited by 356
  4. 2021Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metricscited by 287
  5. 2021On Hallucination and Predictive Uncertainty in Conditional Language Generationcited by 176
  6. 2021A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generationcited by 138
  7. 2022On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?cited by 138
  8. 2021Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarizationcited by 129
  9. 2023Hallucinations in Large Multilingual Translation Modelscited by 129
  10. 2021Entity-level Factual Consistency of Abstractive Text Summarizationcited by 118
  11. 2023TrueTeacher: Learning Factual Consistency Evaluation with Large Language Modelscited by 83
  12. 2021The Factual Inconsistency Problem in Abstractive Text Summarization: A Surveycited by 79
  13. 2022Detecting and Mitigating Hallucinations in Machine Translation: Model Internal Workings Alone Do Well, Sentence Similarity Even Bettercited by 75
  14. 2021Increasing Faithfulness in Knowledge-Grounded Dialogue with Controllable Featurescited by 74
  15. 2022Mutual Information Alleviates Hallucinations in Abstractive Summarizationcited by 68
  16. 2020What Have We Achieved on Text Summarization?cited by 65
  17. 2022Read before Generate! Faithful Long Form Question Answering with Machine Readingcited by 47
  18. 2023Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspectioncited by 44
  19. 2021Controlling Hallucinations at Word Level in Data-to-Text Generationcited by 41
  20. 2023RETA-LLM: A Retrieval-Augmented Large Language Model Toolkitcited by 40
  21. 2020Stolen Probability: A Structural Weakness of Neural Language Modelscited by 22
  22. 2020On Long-Tailed Phenomena in Neural Machine Translationcited by 20
  23. 2022Faithful to the Document or to the World? Mitigating Hallucinations via Entity-linked Knowledge in Abstractive Summarizationcited by 20

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 31; 2025: 40; 2026: 28.

99 papers.

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  1. 2024A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directionscited by 121
  2. 2024Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraphcited by 105
  3. 2024ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretabilitycited by 101
  4. 2024HaloScope: Harnessing Unlabeled LLM Generations for Hallucination Detectioncited by 99
  5. 2024Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Modelscited by 86
  6. 2025Evaluating Step-by-step Reasoning Traces: A Surveycited by 63
  7. 2025A Survey of Uncertainty Estimation Methods on Large Language Modelscited by 62
  8. 2025Large Language Models Hallucination: A Comprehensive Surveycited by 49
  9. 2025Calibrating Verbal Uncertainty as a Linear Feature to Reduce Hallucinationscited by 48
  10. 2024Estimating Knowledge in Large Language Models Without Generating a Single Tokencited by 40
  11. 2024On the Challenges and Opportunities in Generative AIcited by 31
  12. 2024Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspectivecited by 29
38 more
  1. 2025Estimating LLM Uncertainty with Evidencecited by 26
  2. 2024Citation-Enhanced Generation for LLM-based Chatbotscited by 25
  3. 2024RefChecker: Reference-based Fine-grained Hallucination Checker and Benchmark for Large Language Modelscited by 25
  4. 2024Mitigating Large Language Model Hallucination with Faithful Finetuningcited by 24
  5. 2024Atomic Calibration of LLMs in Long-Form Generationscited by 24
  6. 2025TokUR: Token-Level Uncertainty Estimation for Large Language Model Reasoningcited by 24
  7. 2024A Probabilistic Framework for LLM Hallucination Detection via Belief Tree Propagationcited by 21
  8. 2025A Head to Predict and a Head to Question: Pre-trained Uncertainty Quantification Heads for Hallucination Detection in LLM Outputscited by 21
  9. 2025From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centeredcited by 21
  10. 2024Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coveragecited by 20
  11. 2026Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunitiescited by 20
  12. 2024Exploring Response Uncertainty in MLLMs: An Empirical Evaluation under Misleading Scenarioscited by 19
  13. 2026Internal Representations as Indicators of Hallucinations in Agent Tool Selectioncited by 19
  14. 2025Uncertainty Quantification for Language Models: A Suite of Black-Box, White-Box, LLM Judge, and Ensemble Scorerscited by 18
  15. 2024An Audit on the Perspectives and Challenges of Hallucinations in NLPcited by 17
  16. 2024LRP4RAG: Detecting Hallucinations in Retrieval-Augmented Generation via Layer-wise Relevance Propagationcited by 17
  17. 2024Mitigating Hallucinations in Large Language Models via Self-Refinement-Enhanced Knowledge Retrievalcited by 16
  18. 2025Efficient Hallucination Detection for LLMs Using Uncertainty-Aware Attention Headscited by 16
  19. 2024DiaHalu: A Dialogue-level Hallucination Evaluation Benchmark for Large Language Modelscited by 15
  20. 2024Merging Facts, Crafting Fallacies: Evaluating the Contradictory Nature of Aggregated Factual Claims in Long-Form Generationscited by 14
  21. 2025When Models Lie, We Learn: Multilingual Span-Level Hallucination Detection with PsiloQAcited by 14
  22. 2024Machine Translation Hallucination Detection for Low and High Resource Languages using Large Language Modelscited by 13
  23. 2025HalluEntity: Benchmarking and Understanding Entity-Level Hallucination Detectioncited by 13
  24. 2024VaLiD: Mitigating the Hallucination of Large Vision Language Models by Visual Layer Fusion Contrastive Decodingcited by 12
  25. 2025Uncertainty Quantification for Hallucination Detection in Large Language Models: Foundations, Methodology, and Future Directionscited by 11
  26. 2026Reliability-Aware Adaptive Self-Consistency for Efficient Sampling in LLM Reasoningcited by 11
  27. 2024FACTOID: FACtual enTailment fOr hallucInation Detectioncited by 10
  28. 2024Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Modelscited by 9
  29. 2024Zero-resource Hallucination Detection for Text Generation via Graph-based Contextual Knowledge Triples Modelingcited by 9
  30. 2025LUMINA: Detecting Hallucinations in RAG System with Context-Knowledge Signalscited by 9
  31. 2024Prompt-Guided Internal States for Hallucination Detection of Large Language Modelscited by 8
  32. 2025Enhancing Uncertainty Modeling with Semantic Graph for Hallucination Detectioncited by 8
  33. 2025HalluShift: Measuring Distribution Shifts towards Hallucination Detection in LLMscited by 8
  34. 2025Toward Faithful Retrieval-Augmented Generation with Sparse Autoencoderscited by 7
  35. 2025Hallucinated Span Detection with Multi-View Attention Featurescited by 6
  36. 2025HD-NDEs: Neural Differential Equations for Hallucination Detection in LLMscited by 6
  37. 2025Neural Message-Passing on Attention Graphs for Hallucination Detectioncited by 6
  38. 2026Neglected Free Lunch from Post-training: Progress Advantage for LLM Agentscited by 6

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