mlsanityData to 14 Oct 2026

The Landscape of Memorization in LLMs: Mechanisms, Measurement, and Mitigation

arXiv 2507.05578 ↗First posted 8 Jul 2025cs.LG cs.CL cs.CRcited by 10

On the map: LLM safety and security → Machine unlearning

Abstract (arXiv)

Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, yet they also exhibit memorization of their training data. This phenomenon raises critical questions about model behavior, privacy risks, and the boundary between learning and memorization. Addressing these concerns, this paper synthesizes recent studies and investigates the landscape of memorization, the factors influencing it, and methods for its detection and mitigation. We explore key drivers, including training data duplication, training dynamics, and fine-tuning procedures that influence data memorization. In addition, we examine methodologies such as prefix-based extraction, membership inference, and adversarial prompting, assessing their effectiveness in detecting and measuring memorized content. Beyond technical analysis, we also explore the broader implications of memorization, including the legal and ethical implications. Finally, we discuss mitigation strategies, including data cleaning, differential privacy, and post-training unlearning, while highlighting open challenges in balancing the need to minimize harmful memorization with model utility. This paper provides a comprehensive overview of the current state of research on LLM memorization across technical, privacy, and performance dimensions, identifying critical directions for future work.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Placed in Machine unlearning, one of 12 directions in LLM safety and security. Builds on 117 of its 157 references that are in the map. First posted on arXiv 8 Jul 2025.

Builds on

117 of 157 references.

Closest ancestors · 9

Same direction, newest first

  1. 2025Towards Reliable Forgetting: A Survey on Machine Unlearning Verification18 Jun
  2. 2025Towards LLM Unlearning Resilient to Relearning Attacks: A Sharpness-Aware Minimization Perspective and Beyond7 Feb
  3. 2024Verification of Machine Unlearning is Fragile1 Aug
  4. 2024Rethinking LLM Memorization through the Lens of Adversarial Compression23 Apr
  5. 2024Machine Unlearning of Pre-trained Large Language Models23 Feb
  6. 2023Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers27 Jan
  7. 2022Verifiable and Provably Secure Machine Unlearning17 Oct
  8. 2022Knowledge Unlearning for Mitigating Privacy Risks in Language Models4 Oct
  9. 2022A Survey of Machine Unlearning6 Sep

Other references · 108

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2017Proximal Policy Optimization Algorithmscited by 15,215
  4. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  5. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  6. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  7. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  8. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  9. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  10. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
  11. 2016Deep Learning with Differential Privacycited by 2,728
  12. 2023A Survey of Large Language Modelscited by 2,576
38 more
  1. 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,411
  2. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  3. 2020Extracting Training Data from Large Language Modelscited by 2,189
  4. 2019Language Models as Knowledge Bases?cited by 1,936
  5. 2017Understanding Black-box Predictions via Influence Functionscited by 1,741
  6. 2023A Survey on Evaluation of Large Language Modelscited by 1,242
  7. 2024Tulu 3: Pushing Frontiers in Open Language Model Post-Trainingcited by 1,053
  8. 2022Quantifying Memorization Across Neural Language Modelscited by 763
  9. 2021Deduplicating Training Data Makes Language Models Bettercited by 714
  10. 2023A Comprehensive Overview of Large Language Modelscited by 648
  11. 2019Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networkscited by 618
  12. 2021Knowledge Neurons in Pretrained Transformerscited by 601
  13. 2022Transformer Feed-Forward Layers Build Predictions by Promoting Concepts in the Vocabulary Spacecited by 532
  14. 2024Large Language Models: A Surveycited by 525
  15. 2020What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimationcited by 497
  16. 2024Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Researchcited by 483
  17. 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 463
  18. 2023Scalable Extraction of Training Data from (Production) Language Modelscited by 434
  19. 2021Large Language Models Can Be Strong Differentially Private Learnerscited by 412
  20. 2021Differentially Private Fine-tuning of Language Modelscited by 400
  21. 2023Detecting Pretraining Data from Large Language Modelscited by 387
  22. 2023A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Uglycited by 387
  23. 2023Explainability for Large Language Models: A Surveycited by 333
  24. 2023Large Language Models for Information Retrieval: A Surveycited by 331
  25. 2022Deduplicating Training Data Mitigates Privacy Risks in Language Modelscited by 314
  26. 2023Analyzing Leakage of Personally Identifiable Information in Language Modelscited by 285
  27. 2023Membership Inference Attacks against Language Models via Neighbourhood Comparisoncited by 258
  28. 2022Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Modelscited by 220
  29. 2025Learning to Reason without External Rewardscited by 210
  30. 2023Understanding and Mitigating Copying in Diffusion Modelscited by 209
  31. 2023Evaluating Large Language Models: A Comprehensive Surveycited by 202
  32. 2022What Does it Mean for a Language Model to Preserve Privacy?cited by 195
  33. 2024Do Membership Inference Attacks Work on Large Language Models?cited by 195
  34. 2024Security and Privacy Challenges of Large Language Models: A Surveycited by 186
  35. 2023Beyond Memorization: Violating Privacy Via Inference with Large Language Modelscited by 175
  36. 2021Counterfactual Memorization in Neural Language Modelscited by 156
  37. 2022Scaling Laws and Interpretability of Learning from Repeated Datacited by 152
  38. 2023Emergent and Predictable Memorization in Large Language Modelscited by 148

50 most cited of 108 All 108 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 9.

10 papers.

  1. 2025Imitative Membership Inference Attackcited by 3
  2. 2026STaR: Sensitive Trajectory Regulation for Unlearning in Large Reasoning Modelscited by 2
  3. 2026Mitigating Memorization Where It Happenscited by 2
  4. 2026GR-SAP: Generative Replay for Safety Alignment Preservation during Fine-Tuningcited by 2
  5. 2026Predictable Confabulations: Factual Recall by LLMs Scales with Model Size and Topic Frequencycited by 1
  6. 2026LLM-Metrics: Measuring Research Impact Through Large Language Model Memorycited by 0
  7. 2026Extracting Training Data from Diffusion Language Models via Infillingcited by 0
  8. 2026Leak-Resistant Unlearning: A New Benchmark for Evaluating Multi-Hop Reasoning Consistency and Recovery Robustnesscited by 0
  9. 2026Memorization Is Not Extraction: Tight Differential-Privacy Bounds and Audit Blind Spotscited by 0
  10. 2026Mitigating Private Data Leakage in LLMs with Whiteoutcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Rethinking Memorization Measures and their Implications in Large Language Models
  2. 2024Mitigating Memorization In Language Models
  3. 2025Memorization in Fine-Tuned Large Language Models
  4. 2025Hubble: a Model Suite to Advance the Study of LLM Memorization
  5. 2025PerProb: Indirectly Evaluating Memorization in Large Language Models ↗
  6. 2025Leaner Training, Lower Leakage: Revisiting Memorization in LLM Fine-Tuning with LoRA
  7. 2026Unintended Memorization of Sensitive Information in Fine-Tuned Language Models
  8. 2025A Survey on Unlearning in Large Language Models