mlsanityData to 7 Oct 2026

Lynx: An Open Source Hallucination Evaluation Model

arXiv 2407.08488 ↗First posted 11 Jul 2024cs.AI cs.CLcited by 55

On the map: Retrieval-augmented generation → Hallucination detection in RAG

Abstract (arXiv)

Retrieval Augmented Generation (RAG) techniques aim to mitigate hallucinations in Large Language Models (LLMs). However, LLMs can still produce information that is unsupported or contradictory to the retrieved contexts. We introduce LYNX, a SOTA hallucination detection LLM that is capable of advanced reasoning on challenging real-world hallucination scenarios. To evaluate LYNX, we present HaluBench, a comprehensive hallucination evaluation benchmark, consisting of 15k samples sourced from various real-world domains. Our experiment results show that LYNX outperforms GPT-4o, Claude-3-Sonnet, and closed and open-source LLM-as-a-judge models on HaluBench. We release LYNX, HaluBench and our evaluation code for public access.

Key facts. Cited by 55 papers in the map (data through 7 Oct 2026). Placed in Hallucination detection in RAG, one of 15 directions in Retrieval-augmented generation. Builds on 28 of its 40 references that are in the map. First posted on arXiv 11 Jul 2024.

Builds on

28 of 40 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models31 Dec

Other references · 27

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  3. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  4. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
  5. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,205
  6. 2019Language Models as Knowledge Bases?cited by 1,922
  7. 2019PubMedQA: A Dataset for Biomedical Research Question Answeringcited by 1,271
  8. 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,196
  9. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,103
  10. 2019DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphscited by 883
  11. 2021BARTScore: Evaluating Generated Text as Text Generationcited by 719
  12. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691
15 more
  1. 2020Recipes for building an open-domain chatbotcited by 593
  2. 2019Evaluating the Factual Consistency of Abstractive Text Summarizationcited by 578
  3. 2023Prometheus: Inducing Fine-grained Evaluation Capability in Language Modelscited by 558
  4. 2019MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distancecited by 415
  5. 2023Ragas: Automated Evaluation of Retrieval Augmented Generationcited by 408
  6. 2023JudgeLM: Fine-tuned Large Language Models are Scalable Judgescited by 351
  7. 2023FinanceBench: A New Benchmark for Financial Question Answeringcited by 261
  8. 2023ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systemscited by 259
  9. 2021Measuring Attribution in Natural Language Generation Modelscited by 253
  10. 2022On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?cited by 137
  11. 2021Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarizationcited by 128
  12. 2018ROUGE 2.0: Updated and Improved Measures for Evaluation of Summarization Taskscited by 66
  13. 2017Neural Natural Language Inference Models Enhanced with External Knowledgecited by 40
  14. 2023A Comparative Study of Open-Source Large Language Models, GPT-4 and Claude 2: Multiple-Choice Test Taking in Nephrologycited by 26
  15. 2022Embedding Hallucination for Few-Shot Language Fine-tuningcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 8; 2025: 27; 2026: 20.

55 papers.

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  1. 2025MedHallu: A Comprehensive Benchmark for Detecting Medical Hallucinations in Large Language Modelscited by 33
  2. 2024LMUnit: Fine-grained Evaluation with Natural Language Unit Testscited by 32
  3. 2026Noisy but Valid: Robust Statistical Evaluation of LLMs with Imperfect Judgescited by 23
  4. 2025Does Context Matter? ContextualJudgeBench for Evaluating LLM-based Judges in Contextual Settingscited by 22
  5. 2025Reconsidering LLM Uncertainty Estimation Methods in the Wildcited by 17
  6. 2024Differential Transformercited by 8
  7. 2025Mitigating Manipulation and Enhancing Persuasion: A Reflective Multi-Agent Approach for Legal Argument Generationcited by 8
  8. 2024A Comparative Study of DSPy Teleprompter Algorithms for Aligning Large Language Models Evaluation Metrics to Human Evaluationcited by 7
  9. 2025Can LLMs Be Trusted for Evaluating RAG Systems? A Survey of Methods and Datasetscited by 7
  10. 2025Passage Segmentation of Documents for Extractive Question Answeringcited by 6
  11. 2025Loki's Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Modelscited by 6
  12. 2026FACTUM: Mechanistic Detection of Citation Hallucination in Long-Form RAGcited by 6
38 more
  1. 2025Hybrid Retrieval for Hallucination Mitigation in Large Language Models: A Comparative Analysiscited by 5
  2. 2025Learning to Reason for Hallucination Span Detectioncited by 5
  3. 2024Provenance: A Light-weight Fact-checker for Retrieval Augmented LLM Generation Outputcited by 4
  4. 2025TruthTorchLM: A Comprehensive Library for Predicting Truthfulness in LLM Outputscited by 4
  5. 2025Can LLMs Detect Their Confabulations? Estimating Reliability in Uncertainty-Aware Language Modelscited by 4
  6. 2025Representation-based Broad Hallucination Detectors Fail to Generalize Out of Distributioncited by 4
  7. 2026FinGround: Detecting and Grounding Financial Hallucinations via Atomic Claim Verificationcited by 4
  8. 2025HalluMix: A Task-Agnostic, Multi-Domain Benchmark for Real-World Hallucination Detectioncited by 3
  9. 2025Teaching with Lies: Curriculum DPO on Synthetic Negatives for Hallucination Detectioncited by 3
  10. 2025Reasoning's Razor: Reasoning Improves Accuracy but Can Hurt Recall at Critical Operating Points in Safety and Hallucination Detectioncited by 3
  11. 2025Large Language Models as Search Engines: Societal Challengescited by 3
  12. 2026Real-Time Trustworthiness Scoring for LLM Structured Outputs and Data Extractioncited by 3
  13. 2026PARALLAX: Separating Genuine Hallucination Detection from Benchmark Construction Artifactscited by 3
  14. 2025MultiHal: Multilingual Dataset for Knowledge-Graph Grounded Evaluation of LLM Hallucinationscited by 2
  15. 2025HalluGuard: Evidence-Grounded Small Reasoning Models to Mitigate Hallucinations in Retrieval-Augmented Generationcited by 2
  16. 2024Measuring the Groundedness of Legal Question-Answering Systemscited by 1
  17. 2024VERITAS: A Unified Approach to Reliability Evaluationcited by 1
  18. 2024RAGulator: Lightweight Out-of-Context Detectors for Grounded Text Generationcited by 1
  19. 2025Bi'an: A Bilingual Benchmark and Model for Hallucination Detection in Retrieval-Augmented Generationcited by 1
  20. 2025ResNetVLLM-2: Addressing ResNetVLLM's Multi-Modal Hallucinationscited by 1
  21. 2025SCAN: Structured Capability Assessment and Navigation for LLMscited by 1
  22. 2025The Gray Zone of Faithfulness: Taming Ambiguity in Unfaithfulness Detectioncited by 1
  23. 2025HalluClean: A Unified Framework to Combat Hallucinations in LLMscited by 1
  24. 2026A Geometric Taxonomy of Hallucinations in LLMscited by 1
  25. 2026Luna-2: Scalable Single-Token Evaluation with Small Language Modelscited by 1
  26. 2026Probing for Knowledge Attribution in Large Language Modelscited by 1
  27. 2026Retromorphic Testing with Hierarchical Verification for Hallucination Detection in RAGcited by 1
  28. 2026CuraView: A Multi-Agent Framework for Medical Hallucination Detection with GraphRAG-Enhanced Knowledge Verificationcited by 1
  29. 2026LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AIcited by 1
  30. 2026Beyond Document Grounding: Span-Level Hallucination Detection over Code, Tool Output, and Documentscited by 1
  31. 2024How to Choose a Threshold for an Evaluation Metric for Large Language Modelscited by 0
  32. 2025Osiris: A Lightweight Open-Source Hallucination Detection Systemcited by 0
  33. 2025TUM-MiKaNi at SemEval-2025 Task 3: Towards Multilingual and Knowledge-Aware Non-factual Hallucination Identificationcited by 0
  34. 2025HAD: HAllucination Detection Language Models Based on a Comprehensive Hallucination Taxonomycited by 0
  35. 2025From Detection to Diagnosis: Advancing Hallucination Analysis with Automated Data Synthesiscited by 0
  36. 2026Token-Guard: Towards Token-Level Hallucination Control via Self-Checking Decodingcited by 0
  37. 2026POaaS: Minimal-Edit Prompt Optimization as a Service to Lift Accuracy and Cut Hallucinations on On-Device sLLMscited by 0
  38. 2026Robotics-Inspired Guardrails for Foundation Models in Socially Sensitive Domainscited by 0

50 most cited of 55 All 55 (JSON)

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  1. 2023HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models
  2. 2025HalluSearch at SemEval-2025 Task 3: A Search-Enhanced RAG Pipeline for Hallucination Detection
  3. 2024Luna: An Evaluation Foundation Model to Catch Language Model Hallucinations with High Accuracy and Low Cost
  4. 2025AutoRAG-LoRA: Hallucination-Triggered Knowledge Retuning via Lightweight Adapters
  5. 2024THaMES: An End-to-End Tool for Hallucination Mitigation and Evaluation in Large Language Models
  6. 2025LUMINA: Detecting Hallucinations in RAG System with Context-Knowledge Signals
  7. 2024ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretability
  8. 2025ORION Grounded in Context: Retrieval-Based Method for Hallucination Detection