mlsanityData to 7 Oct 2026

Are Large Pre-Trained Language Models Leaking Your Personal Information?

arXiv 2205.12628 ↗First posted 25 May 2022cs.CL cs.AI cs.CRcited by 195

Not on the map. Why?

Cited by 45 in LLM safety and security, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Are Large Pre-Trained Language Models Leaking Your Personal Information? In this paper, we analyze whether Pre-Trained Language Models (PLMs) are prone to leaking personal information. Specifically, we query PLMs for email addresses with contexts of the email address or prompts containing the owner's name. We find that PLMs do leak personal information due to memorization. However, since the models are weak at association, the risk of specific personal information being extracted by attackers is low. We hope this work could help the community to better understand the privacy risk of PLMs and bring new insights to make PLMs safe.

Key facts. Cited by 195 papers in the map (data through 7 Oct 2026). Builds on 20 of its 31 references that are in the map. First posted on arXiv 25 May 2022.

Builds on

20 of 31 references.

References · 20

  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. 2016Deep Learning with Differential Privacycited by 2,682
  4. 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,346
  5. 2020Extracting Training Data from Large Language Modelscited by 2,144
  6. 2019Language Models as Knowledge Bases?cited by 1,922
  7. 2019How Can We Know What Language Models Know?cited by 953
  8. 2022Quantifying Memorization Across Neural Language Modelscited by 755
  9. 2021Deduplicating Training Data Makes Language Models Bettercited by 705
  10. 2020Pre-trained Models for Natural Language Processing: A Surveycited by 504
  11. 2019ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmissioncited by 487
  12. 2021Large Language Models Can Be Strong Differentially Private Learnerscited by 405
8 more
  1. 2021Differentially Private Fine-tuning of Language Modelscited by 392
  2. 2022What Does it Mean for a Language Model to Preserve Privacy?cited by 194
  3. 2020X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Modelscited by 117
  4. 2021Large-Scale Differentially Private BERTcited by 117
  5. 2020Language Models are Open Knowledge Graphscited by 95
  6. 2021Does BERT Pretrained on Clinical Notes Reveal Sensitive Data?cited by 91
  7. 2019Inducing Relational Knowledge from BERTcited by 75
  8. 2021Selective Differential Privacy for Language Modelingcited by 61

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 41; 2024: 71; 2025: 57; 2026: 22.

195 papers.

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  1. 2023Pythia: A Suite for Analyzing Large Language Models Across Training and Scalingcited by 1,734
  2. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,103
  3. 2023Large Language Models Cannot Self-Correct Reasoning Yetcited by 965
  4. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 667
  5. 2022Towards Reasoning in Large Language Models: A Surveycited by 593
  6. 2024TOFU: A Task of Fictitious Unlearning for LLMscited by 507
  7. 2024Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearningcited by 484
  8. 2022Knowledge Unlearning for Mitigating Privacy Risks in Language Modelscited by 424
  9. 2023A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Uglycited by 380
  10. 2023Multi-step Jailbreaking Privacy Attacks on ChatGPTcited by 320
  11. 2023Analyzing Leakage of Personally Identifiable Information in Language Modelscited by 278
  12. 2024Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalizationcited by 203
38 more
  1. 2025Large Language Model Agent: A Survey on Methodology, Applications and Challengescited by 194
  2. 2023Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attackscited by 189
  3. 2024Security and Privacy Challenges of Large Language Models: A Surveycited by 179
  4. 2023A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethicscited by 162
  5. 2024Improving LoRA in Privacy-preserving Federated Learningcited by 158
  6. 2024Fine-grained Hallucination Detection and Editing for Language Modelscited by 146
  7. 2024Benchmark Data Contamination of Large Language Models: A Surveycited by 138
  8. 2023ProPILE: Probing Privacy Leakage in Large Language Modelscited by 127
  9. 2023Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4cited by 123
  10. 2023Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Healthcited by 118
  11. 2025A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deploymentcited by 110
  12. 2024The Art of Saying No: Contextual Noncompliance in Language Modelscited by 100
  13. 2023Language Model Behavior: A Comprehensive Surveycited by 92
  14. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  15. 2024Trustworthiness in Retrieval-Augmented Generation Systems: A Surveycited by 83
  16. 2024Catastrophic Failure of LLM Unlearning via Quantizationcited by 76
  17. 2024SafetyPrompts: a Systematic Review of Open Datasets for Evaluating and Improving Large Language Model Safetycited by 75
  18. 2024Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?cited by 68
  19. 2024Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systemscited by 63
  20. 2024Building Guardrails for Large Language Modelscited by 61
  21. 2023Why Does ChatGPT Fall Short in Providing Truthful Answers?cited by 54
  22. 2024A Closer Look at Machine Unlearning for Large Language Modelscited by 54
  23. 2024CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue Coreferencecited by 51
  24. 2023Privacy in Large Language Models: Attacks, Defenses and Future Directionscited by 48
  25. 2023Reducing Privacy Risks in Online Self-Disclosures with Language Modelscited by 47
  26. 2023Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Eventscited by 46
  27. 2023Tricking LLMs into Disobedience: Formalizing, Analyzing, and Detecting Jailbreakscited by 45
  28. 2023Training Data Extraction From Pre-trained Language Models: A Surveycited by 43
  29. 2025A Survey on Personalized Alignment -- The Missing Piece for Large Language Models in Real-World Applicationscited by 40
  30. 2024LLM-PBE: Assessing Data Privacy in Large Language Modelscited by 38
  31. 2023RAVEN: In-Context Learning with Retrieval-Augmented Encoder-Decoder Language Modelscited by 36
  32. 2024Securing Large Language Models: Threats, Vulnerabilities and Responsible Practicescited by 36
  33. 2024Feedback-Guided Extraction of Knowledge Base from Retrieval-Augmented LLM Applicationscited by 36
  34. 2023The Janus Interface: How Fine-Tuning in Large Language Models Amplifies the Privacy Riskscited by 35
  35. 2023PrivLM-Bench: A Multi-level Privacy Evaluation Benchmark for Language Modelscited by 35
  36. 2023Knowledge Sanitization of Large Language Modelscited by 34
  37. 2024Graph Machine Learning in the Era of Large Language Models (LLMs)cited by 33
  38. 2024Revisiting Who's Harry Potter: Towards Targeted Unlearning from a Causal Intervention Perspectivecited by 33

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