mlsanityData to 7 Oct 2026

Detecting Hallucinated Content in Conditional Neural Sequence Generation

arXiv 2011.02593 ↗First posted 5 Nov 2020cs.CL cs.AIcited by 151

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Abstract (arXiv)

Neural sequence models can generate highly fluent sentences, but recent studies have also shown that they are also prone to hallucinate additional content not supported by the input. These variety of fluent but wrong outputs are particularly problematic, as it will not be possible for users to tell they are being presented incorrect content. To detect these errors, we propose a task to predict whether each token in the output sequence is hallucinated (not contained in the input) and collect new manually annotated evaluation sets for this task. We also introduce a method for learning to detect hallucinations using pretrained language models fine tuned on synthetic data that includes automatically inserted hallucinations Experiments on machine translation (MT) and abstractive summarization demonstrate that our proposed approach consistently outperforms strong baselines on all benchmark datasets. We further demonstrate how to use the token-level hallucination labels to define a fine-grained loss over the target sequence in low-resource MT and achieve significant improvements over strong baseline methods. We also apply our method to word-level quality estimation for MT and show its effectiveness in both supervised and unsupervised settings. Codes and data available at https://github.com/violet-zct/fairseq-detect-hallucination.

Key facts. Cited by 151 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Nov 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 23; 2022: 21; 2023: 42; 2024: 47; 2025: 16; 2026: 2.

151 papers.

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  1. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
  2. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  3. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,665
  4. 2021Generalized Out-of-Distribution Detection: A Surveycited by 796
  5. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 751
  6. 2023Aligning Large Multimodal Models with Factually Augmented RLHFcited by 703
  7. 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 695
  8. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691
  9. 2023Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decodingcited by 671
  10. 2023Active Retrieval Augmented Generationcited by 506
  11. 2021Get Your Vitamin C! Robust Fact Verification with Contrastive Evidencecited by 222
  12. 2021Hurdles to Progress in Long-form Question Answeringcited by 166
38 more
  1. 2021A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generationcited by 137
  2. 2021Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarizationcited by 128
  3. 2023Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigationcited by 123
  4. 2021Measure and Improve Robustness in NLP Models: A Surveycited by 107
  5. 2022ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reportscited by 100
  6. 2023SAC3: Reliable Hallucination Detection in Black-Box Language Models via Semantic-aware Cross-check Consistencycited by 89
  7. 2024VideoHallucer: Evaluating Intrinsic and Extrinsic Hallucinations in Large Video-Language Modelscited by 88
  8. 2024Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Modelscited by 84
  9. 2021The Factual Inconsistency Problem in Abstractive Text Summarization: A Surveycited by 79
  10. 2022Detecting and Mitigating Hallucinations in Machine Translation: Model Internal Workings Alone Do Well, Sentence Similarity Even Bettercited by 75
  11. 2022Mutual Information Alleviates Hallucinations in Abstractive Summarizationcited by 67
  12. 2024DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language Modelscited by 63
  13. 2024Navigating the Risks: A Survey of Security, Privacy, and Ethics Threats in LLM-Based Agentscited by 63
  14. 2021Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generationcited by 60
  15. 2022Looking for a Needle in a Haystack: A Comprehensive Study of Hallucinations in Neural Machine Translationcited by 60
  16. 2023Red Teaming Language Model Detectors with Language Modelscited by 55
  17. 2021Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarizationcited by 50
  18. 2023TIM: Teaching Large Language Models to Translate with Comparisoncited by 50
  19. 2023Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspectioncited by 44
  20. 2023Exploring Human-Like Translation Strategy with Large Language Modelscited by 44
  21. 2024Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Modelscited by 44
  22. 2023DelucionQA: Detecting Hallucinations in Domain-specific Question Answeringcited by 43
  23. 2023SEAHORSE: A Multilingual, Multifaceted Dataset for Summarization Evaluationcited by 42
  24. 2023RETA-LLM: A Retrieval-Augmented Large Language Model Toolkitcited by 40
  25. 2023HalOmi: A Manually Annotated Benchmark for Multilingual Hallucination and Omission Detection in Machine Translationcited by 35
  26. 2023Chain of Natural Language Inference for Reducing Large Language Model Ungrounded Hallucinationscited by 34
  27. 2024AI Hallucinations: A Misnomer Worth Clarifyingcited by 34
  28. 2024Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMscited by 34
  29. 2024Alleviating Hallucinations in Large Vision-Language Models through Hallucination-Induced Optimizationcited by 34
  30. 2021CRASS: A Novel Data Set and Benchmark to Test Counterfactual Reasoning of Large Language Modelscited by 33
  31. 2023Evaluating Hallucinations in Chinese Large Language Modelscited by 33
  32. 2023TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoningcited by 32
  33. 2023HallE-Control: Controlling Object Hallucination in Large Multimodal Modelscited by 31
  34. 2024Improving Uncertainty Quantification in Large Language Models via Semantic Embeddingscited by 30
  35. 2021Rome was built in 1776: A Case Study on Factual Correctness in Knowledge-Grounded Response Generationcited by 29
  36. 2022Learning to Revise References for Faithful Summarizationcited by 28
  37. 2022Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methodscited by 27
  38. 2024HaluEval-Wild: Evaluating Hallucinations of Language Models in the Wildcited by 27

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