mlsanityData to 7 Oct 2026

Demystifying Verbatim Memorization in Large Language Models

arXiv 2407.17817 ↗First posted 25 Jul 2024cs.CL cs.LGcited by 55

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Abstract (arXiv)

Large Language Models (LLMs) frequently memorize long sequences verbatim, often with serious legal and privacy implications. Much prior work has studied such verbatim memorization using observational data. To complement such work, we develop a framework to study verbatim memorization in a controlled setting by continuing pre-training from Pythia checkpoints with injected sequences. We find that (1) non-trivial amounts of repetition are necessary for verbatim memorization to happen; (2) later (and presumably better) checkpoints are more likely to verbatim memorize sequences, even for out-of-distribution sequences; (3) the generation of memorized sequences is triggered by distributed model states that encode high-level features and makes important use of general language modeling capabilities. Guided by these insights, we develop stress tests to evaluate unlearning methods and find they often fail to remove the verbatim memorized information, while also degrading the LM. Overall, these findings challenge the hypothesis that verbatim memorization stems from specific model weights or mechanisms. Rather, verbatim memorization is intertwined with the LM's general capabilities and thus will be very difficult to isolate and suppress without degrading model quality.

Key facts. Cited by 55 papers in the map (data through 7 Oct 2026). Builds on 38 of its 52 references that are in the map. First posted on arXiv 25 Jul 2024.

Builds on

38 of 52 references.

References · 38

  1. 2022Locating and Editing Factual Associations in GPTcited by 2,609
  2. 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,346
  3. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  4. 2023Pythia: A Suite for Analyzing Large Language Models Across Training and Scalingcited by 1,734
  5. 2016Understanding deep learning requires rethinking generalizationcited by 1,657
  6. 2020Transformer Feed-Forward Layers Are Key-Value Memoriescited by 1,307
  7. 2017A Closer Look at Memorization in Deep Networkscited by 936
  8. 2018The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networkscited by 847
  9. 2021Deduplicating Training Data Makes Language Models Bettercited by 705
  10. 2023Dissecting Recall of Factual Associations in Auto-Regressive Language Modelscited by 512
  11. 2024TOFU: A Task of Fictitious Unlearning for LLMscited by 507
  12. 2020What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimationcited by 492
26 more
  1. 2019Does Learning Require Memorization? A Short Tale about a Long Tailcited by 452
  2. 2023Scalable Extraction of Training Data from (Production) Language Modelscited by 415
  3. 2021Causal Abstractions of Neural Networkscited by 401
  4. 2013Direct and Indirect Effectscited by 276
  5. 2024Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Modelscited by 225
  6. 2023Unlearn What You Want to Forget: Efficient Unlearning for LLMscited by 211
  7. 2024Eight Methods to Evaluate Robust Unlearning in LLMscited by 153
  8. 2024Infini-gram: Scaling Unbounded n-gram Language Models to a Trillion Tokenscited by 148
  9. 2024Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Lawscited by 137
  10. 2023Sudden Drops in the Loss: Syntax Acquisition, Phase Transitions, and Simplicity Bias in MLMscited by 121
  11. 2022Measuring Forgetting of Memorized Training Examplescited by 118
  12. 2020When is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning?cited by 102
  13. 2023Scan and Snap: Understanding Training Dynamics and Token Composition in 1-layer Transformercited by 101
  14. 2022Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacycited by 97
  15. 2024How Do Large Language Models Acquire Factual Knowledge During Pretraining?cited by 86
  16. 2021Similarity and Matching of Neural Network Representationscited by 77
  17. 2023Can Neural Network Memorization Be Localized?cited by 66
  18. 2022Understanding Transformer Memorization Recall Through Idiomscited by 62
  19. 2024Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMscited by 50
  20. 2024Detection and Measurement of Syntactic Templates in Generated Textcited by 44
  21. 2024CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model Generationcited by 35
  22. 2024Alpaca against Vicuna: Using LLMs to Uncover Memorization of LLMscited by 33
  23. 2024Localizing Paragraph Memorization in Language Modelscited by 30
  24. 2024Recite, Reconstruct, Recollect: Memorization in LMs as a Multifaceted Phenomenoncited by 30
  25. 2023Data Portraits: Recording Foundation Model Training Datacited by 29
  26. 2023Memorisation Cartography: Mapping out the Memorisation-Generalisation Continuum in Neural Machine Translationcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 9; 2025: 30; 2026: 16.

55 papers.

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  1. 2024Protecting Privacy in Multimodal Large Language Models with MLLMU-Benchcited by 61
  2. 2024Position: LLM Unlearning Benchmarks are Weak Measures of Progresscited by 58
  3. 2024Measuring memorization in language models via probabilistic extractioncited by 40
  4. 2025Emergent Abilities in Large Language Models: A Surveycited by 34
  5. 2024Undesirable Memorization in Large Language Models: A Surveycited by 29
  6. 2025The Reasoning-Memorization Interplay in Language Models Is Mediated by a Single Directioncited by 21
  7. 2025Privacy Attacks on Image AutoRegressive Modelscited by 15
  8. 2025Data Mixing Can Induce Phase Transitions in Knowledge Acquisitioncited by 15
  9. 2025Position: Privacy Is Not Just Memorization!cited by 15
  10. 2024How Much Can We Forget about Data Contamination?cited by 14
  11. 2025Blackbox Model Provenance via Palimpsestic Membership Inferencecited by 11
  12. 2025Privacy Ripple Effects from Adding or Removing Personal Information in Language Model Trainingcited by 9
38 more
  1. 2025The Landscape of Memorization in LLMs: Mechanisms, Measurement, and Mitigationcited by 9
  2. 2026Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retentioncited by 8
  3. 2024Understanding and Mitigating Memorization in Diffusion Models for Tabular Datacited by 7
  4. 2025Extracting alignment data in open modelscited by 7
  5. 2025Mitigating Unintended Memorization with LoRA in Federated Learning for LLMscited by 6
  6. 2025ParaPO: Aligning Language Models to Reduce Verbatim Reproduction of Pre-training Datacited by 6
  7. 2025Measuring LLM Novelty As The Frontier Of Original And High-Quality Outputcited by 5
  8. 2024Get Confused Cautiously: Textual Sequence Memorization Erasure with Selective Entropy Maximizationcited by 4
  9. 2025Machine Learners Should Acknowledge the Legal Implications of Large Language Models as Personal Datacited by 4
  10. 2025SUV: Scalable Large Language Model Copyright Compliance with Regularized Selective Unlearningcited by 4
  11. 2025FLAME-MoE: A Transparent End-to-End Research Platform for Mixture-of-Experts Language Modelscited by 4
  12. 2025SUA: Stealthy Multimodal Large Language Model Unlearning Attackcited by 4
  13. 2025Realizing LLMs' Causal Potential Requires Science-Grounded, Novel Benchmarkscited by 4
  14. 2024Detecting Memorization in Large Language Modelscited by 3
  15. 2025OWL: Probing Cross-Lingual Recall of Memorized Texts via World Literaturecited by 3
  16. 2025Distributional Semantics Tracing: A Framework for Explaining Hallucinations in Large Language Modelscited by 3
  17. 2025Retracing the Past: LLMs Emit Training Data When They Get Lostcited by 3
  18. 2026Characterizing Memorization in Diffusion Language Models: Generalized Extraction and Sampling Effectscited by 3
  19. 2024Neuron-Level Differentiation of Memorization and Generalization in Large Language Modelscited by 2
  20. 2025Positional Fragility in LLMs: How Offset Effects Reshape Our Understanding of Memorization Riskscited by 2
  21. 2025Mirage of Mastery: Memorization Tricks LLMs into Artificially Inflated Self-Knowledgecited by 2
  22. 2025Direct Token Optimization: A Self-contained Approach to Large Language Model Unlearningcited by 2
  23. 2026Memorization Dynamics in Knowledge Distillation for Language Modelscited by 2
  24. 2026GSS: Gated Subspace Steering for Selective Memorization Mitigation in LLMscited by 2
  25. 2026Humans and transformer LMs: Abstraction drives language learningcited by 2
  26. 2026Position: Don't Just "Fix it in Post": A Science of AI Must Study Training Dynamicscited by 2
  27. 2025A Lightweight Method to Disrupt Memorized Sequences in LLMcited by 1
  28. 2025MIAT: Maneuver-Intention-Aware Transformer for Spatio-Temporal Trajectory Predictioncited by 1
  29. 2025A Closer Look on Memorization in Tabular Diffusion Model: A Data-Centric Perspectivecited by 1
  30. 2025Data Compressibility Quantifies LLM Memorizationcited by 1
  31. 2025From Memorization to Reasoning in the Spectrum of Loss Curvaturecited by 1
  32. 2025Designing AI-Resilient Assessments Using Interconnected Problems: A Theoretically Grounded and Empirically Validated Frameworkcited by 1
  33. 2026Quantifying the Effect of Test Set Contamination on Generative Evaluationscited by 1
  34. 2026Exposing Long-Tail Safety Failures in Large Language Models through Efficient Diverse Response Samplingcited by 1
  35. 2026LLMs Can Leak Training Data But Do They Want To? A Propensity-Aware Evaluation of Memorization in LLMscited by 1
  36. 2025Beyond Frequency: The Role of Redundancy in Large Language Model Memorizationcited by 0
  37. 2026UniGeM: Unifying Data Mixing and Selection via Geometric Exploration and Miningcited by 0
  38. 2026Memorization Dynamics of Fill-in-the-Middle Pretrainingcited by 0

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