mlsanityData to 7 Oct 2026

InterrogateLLM: Zero-Resource Hallucination Detection in LLM-Generated Answers

arXiv 2403.02889 ↗First posted 5 Mar 2024cs.CL cs.LGcited by 26

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Abstract (arXiv)

Despite the many advances of Large Language Models (LLMs) and their unprecedented rapid evolution, their impact and integration into every facet of our daily lives is limited due to various reasons. One critical factor hindering their widespread adoption is the occurrence of hallucinations, where LLMs invent answers that sound realistic, yet drift away from factual truth. In this paper, we present a novel method for detecting hallucinations in large language models, which tackles a critical issue in the adoption of these models in various real-world scenarios. Through extensive evaluations across multiple datasets and LLMs, including Llama-2, we study the hallucination levels of various recent LLMs and demonstrate the effectiveness of our method to automatically detect them. Notably, we observe up to 87% hallucinations for Llama-2 in a specific experiment, where our method achieves a Balanced Accuracy of 81%, all without relying on external knowledge.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 10 of its 20 references that are in the map. First posted on arXiv 5 Mar 2024.

Builds on

10 of 20 references.

References · 10

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  4. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  5. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
  6. 2022Language Models (Mostly) Know What They Knowcited by 1,783
  7. 2023The Internal State of an LLM Knows When It's Lyingcited by 644
  8. 2019Evaluating the Factual Consistency of Abstractive Text Summarizationcited by 578
  9. 2021A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generationcited by 137
  10. 2023MQAG: Multiple-choice Question Answering and Generation for Assessing Information Consistency in Summarizationcited by 30

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 16; 2026: 8.

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  1. 2024Hallucination Detection in Foundation Models for Decision-Making: A Flexible Definition and Review of the State of the Artcited by 17
  2. 2025Attention-guided Self-reflection for Zero-shot Hallucination Detection in Large Language Modelscited by 16
  3. 2026DynHD: Hallucination Detection for Diffusion Large Language Models via Denoising Dynamics Deviation Learningcited by 13
  4. 2025Conversational User-AI Intervention: A Study on Prompt Rewriting for Improved LLM Response Generationcited by 10
  5. 2024A Survey of Automatic Hallucination Evaluation on Natural Language Generationcited by 7
  6. 2025Consistency Is the Key: Detecting Hallucinations in LLM Generated Text By Checking Inconsistencies About Key Factscited by 4
  7. 2025SEReDeEP: Hallucination Detection in Retrieval-Augmented Models via Semantic Entropy and Context-Parameter Fusioncited by 3
  8. 2025Towards Unification of Hallucination Detection and Fact Verification for Large Language Modelscited by 2
  9. 2025TPA: Next Token Probability Attribution for Detecting Hallucinations in RAGcited by 2
  10. 2026Who Checks the Citations? Benchmarking Legal Hallucination Detectioncited by 2
  11. 2025HalluCounter: Reference-free LLM Hallucination Detection in the Wild!cited by 1
  12. 2025Learning Auxiliary Tasks Improves Reference-Free Hallucination Detection in Open-Domain Long-Form Generationcited by 1
14 more
  1. 2025Eidoku: A Neuro-Symbolic Verification Gate for LLM Reasoning via Structural Constraint Satisfactioncited by 1
  2. 2026Beyond In-Domain Detection: SpikeScore for Cross-Domain Hallucination Detectioncited by 1
  3. 2025MMD-Flagger: Leveraging Maximum Mean Discrepancy to Detect Hallucinationscited by 0
  4. 2025Conservative Bias in Large Language Models: Measuring Relation Predictionscited by 0
  5. 2025Towards Building General Purpose Embedding Models for Industry 4.0 Agentscited by 0
  6. 2025Abstract Meaning Representation for Hospital Discharge Summarizationcited by 0
  7. 2025Fake or Real: The Impostor Hunt in Texts for Space Operationscited by 0
  8. 2025Enhancing Hallucination Detection via Future Contextcited by 0
  9. 2025A Concise Review of Hallucinations in LLMs and their Mitigationcited by 0
  10. 2026Disentangling Deception and Hallucination Failures in LLMscited by 0
  11. 2026HART: Data-Driven Hallucination Attribution and Evidence-Based Tracing for Large Language Modelscited by 0
  12. 2026AURORA: Asymmetry and Update-Induced Rotation for Robust Hallucination Detection in Large Language Modelscited by 0
  13. 2026CodeTD: Topology of Attention Detects Hallucinations in Code LLMscited by 0
  14. 2026Toward Complete Hospital Discharge Summarization with Abstract Meaning Representationcited by 0