mlsanityData to 14 Oct 2026

Logical Consistency as a Bridge: Improving LLM Hallucination Detection via Label Constraint Modeling between Responses and Self-Judgments

arXiv 2605.03971 ↗First posted 5 May 2026cs.CLcited by 0

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Abstract (arXiv)

Large Language Models (LLMs) are prone to factual hallucinations, risking their reliability in real-world applications. Existing hallucination detectors mainly extract micro-level intrinsic patterns for uncertainty quantification or elicit macro-level self-judgments through verbalized prompts. However, these methods address only a single facet of the hallucination, focusing either on implicit neural uncertainty or explicit symbolic reasoning, thereby treating these inherently coupled behaviors in isolation and failing to exploit their interdependence for a holistic view. In this paper, we propose LaaB (Logical Consistency-as-a-Bridge), a framework that bridges neural features and symbolic judgments for hallucination detection. LaaB introduces a "meta-judgment" process to map symbolic labels back into the feature space. By leveraging the inherent logical bridge where response and meta-judgment labels are either the same or opposite based on the self-judgment's semantics, LaaB aligns and integrates dual-view signals via mutual learning and enhances the hallucination detection. Extensive experiments on 4 public datasets, across 4 LLMs, against 8 baselines demonstrate the superiority of LaaB.

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References · 43

  1. 2020Measuring Massive Multitask Language Understandingcited by 7,963
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  4. 2022Language Models (Mostly) Know What They Knowcited by 1,856
  5. 2023Inference-Time Intervention: Eliciting Truthful Answers from a Language Modelcited by 1,302
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  8. 2024LLM Evaluators Recognize and Favor Their Own Generationscited by 686
  9. 2023The Internal State of an LLM Knows When It's Lyingcited by 659
  10. 2017Deep Mutual Learningcited by 628
  11. 2024INSIDE: LLMs' Internal States Retain the Power of Hallucination Detectioncited by 323
  12. 2024Self-Preference Bias in LLM-as-a-Judgecited by 258
31 more
  1. 2023FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarioscited by 251
  2. 2023Do Large Language Models Know What They Don't Know?cited by 243
  3. 2024Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Mapscited by 142
  4. 2025Between Underthinking and Overthinking: An Empirical Study of Reasoning Length and correctness in LLMscited by 117
  5. 2024Relying on the Unreliable: The Impact of Language Models' Reluctance to Express Uncertaintycited by 106
  6. 2024HaloScope: Harnessing Unlabeled LLM Generations for Hallucination Detectioncited by 99
  7. 2023Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkerscited by 87
  8. 2024Latent Space Chain-of-Embedding Enables Output-free LLM Self-Evaluationcited by 57
  9. 2025An Agentic System for Rare Disease Diagnosis with Traceable Reasoningcited by 52
  10. 2025Steer LLM Latents for Hallucination Detectioncited by 50
  11. 2024On Large Language Models' Hallucination with Regard to Known Factscited by 43
  12. 2025Hallucination Detection in LLMs Using Spectral Features of Attention Mapscited by 43
  13. 2024Understanding the Dark Side of LLMs' Intrinsic Self-Correctioncited by 42
  14. 2025ConfTuner: Training Large Language Models to Express Their Confidence Verballycited by 39
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  16. 2025ICR Probe: Tracking Hidden State Dynamics for Reliable Hallucination Detection in LLMscited by 32
  17. 2025Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perceptioncited by 27
  18. 2025Trust Me, I'm Wrong: LLMs Hallucinate with Certainty Despite Knowing the Answercited by 27
  19. 2021Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learningcited by 25
  20. 2024On the Universal Truthfulness Hyperplane Inside LLMscited by 24
  21. 2024Ten Words Only Still Help: Improving Black-Box AI-Generated Text Detection via Proxy-Guided Efficient Re-Samplingcited by 20
  22. 2024Towards Detecting LLMs Hallucination via Markov Chain-based Multi-agent Debate Frameworkcited by 19
  23. 2025LLM-Generated Fake News Induces Truth Decay in News Ecosystem: A Case Study on Neural News Recommendationcited by 19
  24. 2025Too Consistent to Detect: A Study of Self-Consistent Errors in LLMscited by 18
  25. 2025On the Fundamental Limits of LLMs at Scalecited by 18
  26. 2024Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Modelscited by 9
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  28. 2026BaseCal: Unsupervised Confidence Calibration via Base Model Signalscited by 8
  29. 2025Simulating Dispute Mediation with LLM-Based Agents for Legal Researchcited by 7
  30. 2025UNCERTAINTY-LINE: Length-Invariant Estimation of Uncertainty for Large Language Modelscited by 5
  31. 2026InFi-Check: Interpretable and Fine-Grained Fact-Checking of LLMscited by 1

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  1. 2025Revisiting Hallucination Detection with Effective Rank-based Uncertainty
  2. 2026UHP Detection: LVLMs have their Unique Hallucination Pattern in the Consistency Space
  3. 2025HARP: Hallucination Detection via Reasoning Subspace Projection
  4. 2025Joint Evaluation of Answer and Reasoning Consistency for Hallucination Detection in Large Reasoning Models
  5. 2025Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations
  6. 2026Scalable Token-Level Hallucination Detection in Large Language Models
  7. 2024Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback
  8. 2026BEACON: Behavioral Entropy Aggregation for Cross-Model Hallucination Detection in Large Language Models