mlsanityData to 14 Oct 2026

Representation-based Broad Hallucination Detectors Fail to Generalize Out of Distribution

arXiv 2509.19372 ↗First posted 19 Sep 2025cs.LG cs.AIcited by 4

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Abstract (arXiv)

We critically assess the efficacy of the current SOTA in hallucination detection and find that its performance on the RAGTruth dataset is largely driven by a spurious correlation with data. Controlling for this effect, state-of-the-art performs no better than supervised linear probes, while requiring extensive hyperparameter tuning across datasets. Out-of-distribution generalization is currently out of reach, with all of the analyzed methods performing close to random. We propose a set of guidelines for hallucination detection and its evaluation.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 26 of its 41 references that are in the map. First posted on arXiv 19 Sep 2025.

Builds on

26 of 41 references.

References · 26

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,975
  3. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,605
  4. 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,554
  5. 2023Inference-Time Intervention: Eliciting Truthful Answers from a Language Modelcited by 1,302
  6. 2015Teaching Machines to Read and Comprehendcited by 1,049
  7. 2024Refusal in Language Models Is Mediated by a Single Directioncited by 1,032
  8. 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 812
  9. 2022Discovering Latent Knowledge in Language Models Without Supervisioncited by 767
  10. 2023The Internal State of an LLM Knows When It's Lyingcited by 659
  11. 2019On Evaluating Adversarial Robustnesscited by 447
  12. 2024Mechanistic Interpretability for AI Safety -- A Reviewcited by 422
14 more
  1. 2024INSIDE: LLMs' Internal States Retain the Power of Hallucination Detectioncited by 323
  2. 2023RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Modelscited by 283
  3. 2023ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systemscited by 265
  4. 2024Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMscited by 259
  5. 2024Retrieval Head Mechanistically Explains Long-Context Factualitycited by 232
  6. 2024Not All Language Model Features Are One-Dimensionally Linearcited by 212
  7. 2024Do I Know This Entity? Knowledge Awareness and Hallucinations in Language Modelscited by 115
  8. 2024ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretabilitycited by 101
  9. 2023Still No Lie Detector for Language Models: Probing Empirical and Conceptual Roadblockscited by 78
  10. 2024Steering Knowledge Selection Behaviours in LLMs via SAE-Based Representation Engineeringcited by 63
  11. 2024Lynx: An Open Source Hallucination Evaluation Modelcited by 57
  12. 2023Challenges with unsupervised LLM knowledge discoverycited by 31
  13. 2024DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate Hallucinationscited by 25
  14. 2024Controllable Context Sensitivity and the Knob Behind Itcited by 23

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Temporal Multi-Signal Fusion for Token-Level Hallucination Detectioncited by 1
  2. 2026UnWeaving the knots of GraphRAG -- turns out VectorRAG is almost enoughcited by 0
  3. 2026Semantic Adapter Routing with Fine-Tuning Task Embeddingscited by 0
  4. 2026Leveraging Low-Level Symbolic Competences for Unsupervised Grounding in Hallucination Detectioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Rethinking Evaluation for LLM Hallucination Detection: A Desiderata, A New RAG-based Benchmark, New Insights
  2. 2026Density Ridge Selective Prediction for LLM and VLM Hallucination Detection under Calibration Label Scarcity
  3. 2025The Semantic Illusion: Certified Limits of Embedding-Based Hallucination Detection in RAG Systems
  4. 2026Diversity-Oriented Fine-Tuning for Uncertainty-Based Hallucination Detection
  5. 2026HalluSAE: Detecting Hallucinations in Large Language Models via Sparse Auto-Encoders
  6. 2025Osiris: A Lightweight Open-Source Hallucination Detection System
  7. 2025MTRE: Multi-Token Reliability Estimation for Hallucination Detection in VLMs
  8. 2026HalluScope: Fine-grained Hallucination Diagnosis for Multimodal Large Language Models