Rethinking Memorization Measures and their Implications in Large Language Models | Paper Citation Record

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This paper cites On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pages 610–623, 2021.

Rethinking Memorization Measures and their Implications in Large Language Models On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pages 610–623, 2021

Reference 1

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This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

Rethinking Memorization Measures and their Implications in Large Language Models Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 2

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This paper cites Rethinking LLM Memorization through the Lens of Adversarial Compression.

Rethinking Memorization Measures and their Implications in Large Language Models Rethinking LLM Memorization through the Lens of Adversarial Compression

Reference 3

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This paper cites Emergent and predictable memorization in large language models.

Rethinking Memorization Measures and their Implications in Large Language Models Emergent and predictable memorization in large language models

Reference 4

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This paper cites Deduplicating training data mitigates privacy risks in language models.

Rethinking Memorization Measures and their Implications in Large Language Models Deduplicating training data mitigates privacy risks in language models

Reference 5

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This paper cites Extracting training data from large language models.

Rethinking Memorization Measures and their Implications in Large Language Models Extracting training data from large language models

Reference 6

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This paper cites The secret sharer: Evaluating and testing unintended memorization in neural networks.

Rethinking Memorization Measures and their Implications in Large Language Models The secret sharer: Evaluating and testing unintended memorization in neural networks

Reference 7

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This paper cites Memorization without overfitting: Analyzing the training dynamics of large language models.

Rethinking Memorization Measures and their Implications in Large Language Models Memorization without overfitting: Analyzing the training dynamics of large language models

Reference 8

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This paper cites An empirical analysis of memorization in fine-tuned autoregressive language models.

Rethinking Memorization Measures and their Implications in Large Language Models An empirical analysis of memorization in fine-tuned autoregressive language models

Reference 9

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This paper cites Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy.

Rethinking Memorization Measures and their Implications in Large Language Models Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy

Reference 10

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This paper cites Near-duplicate sequence search at scale for large language model memorization evaluation.

Rethinking Memorization Measures and their Implications in Large Language Models Near-duplicate sequence search at scale for large language model memorization evaluation

Reference 11

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Observation b22ae51b-c1b1-4919-8f4a-a540e501330e · outbound

This paper cites Uncovering latent memories: Assessing data leakage and memorization patterns in large language models.

Rethinking Memorization Measures and their Implications in Large Language Models Uncovering latent memories: Assessing data leakage and memorization patterns in large language models

Reference 12

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This paper cites Quantifying and analyzing entity-level memorization in large language models.

Rethinking Memorization Measures and their Implications in Large Language Models Quantifying and analyzing entity-level memorization in large language models

Reference 13

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Observation d1e79c42-62da-413e-b5b3-759e0e1d3272 · outbound

This paper cites Counterfactual Memorization in Neural Language Models.

Rethinking Memorization Measures and their Implications in Large Language Models Counterfactual Memorization in Neural Language Models

Reference 14

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This paper cites On memorization in probabilistic deep generative models.

Rethinking Memorization Measures and their Implications in Large Language Models On memorization in probabilistic deep generative models

Reference 15

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This paper cites Deduplicating Training Data Makes Language Models Better.

Rethinking Memorization Measures and their Implications in Large Language Models Deduplicating Training Data Makes Language Models Better

Reference 16

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This paper cites ‘improving ratings’: audit in the british university system.

Rethinking Memorization Measures and their Implications in Large Language Models ‘improving ratings’: audit in the british university system

Reference 17

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Observation 4408f1bf-837b-4ea9-9b9f-696b4f9a79fd · outbound

This paper cites Quantifying Memorization Across Neural Language Models.

Rethinking Memorization Measures and their Implications in Large Language Models Quantifying Memorization Across Neural Language Models

Reference 18

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This paper cites What Languages are Easy to Language-Model? A Perspective from Learning Probabilistic Regular Languages.

Rethinking Memorization Measures and their Implications in Large Language Models What Languages are Easy to Language-Model? A Perspective from Learning Probabilistic Regular Languages

Reference 19

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This paper cites In-Context Language Learning: Architectures and Algorithms.

Rethinking Memorization Measures and their Implications in Large Language Models In-Context Language Learning: Architectures and Algorithms

Reference 20

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This paper cites Transparency at the Source: Evaluating and Interpreting Language Models With Access to the True Distribution.

Rethinking Memorization Measures and their Implications in Large Language Models Transparency at the Source: Evaluating and Interpreting Language Models With Access to the True Distribution

Reference 21

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This paper cites Injecting structural hints: Using language models to study inductive biases in language learning.

Rethinking Memorization Measures and their Implications in Large Language Models Injecting structural hints: Using language models to study inductive biases in language learning

Reference 22

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This paper cites Examining the Inductive Bias of Neural Language Models with Artificial Languages.

Rethinking Memorization Measures and their Implications in Large Language Models Examining the Inductive Bias of Neural Language Models with Artificial Languages

Reference 23

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This paper cites Towards more natural artificial languages.

Rethinking Memorization Measures and their Implications in Large Language Models Towards more natural artificial languages

Reference 24

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This paper cites Physics of Language Models: Part 1, Learning Hierarchical Language Structures.

Rethinking Memorization Measures and their Implications in Large Language Models Physics of Language Models: Part 1, Learning Hierarchical Language Structures

Reference 25

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This paper cites Transformer Working Memory Enables Regular Language Reasoning and Natural Language Length Extrapolation.

Rethinking Memorization Measures and their Implications in Large Language Models Transformer Working Memory Enables Regular Language Reasoning and Natural Language Length Extrapolation

Reference 26

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This paper cites Characterizing Intrinsic Compositionality in Transformers with Tree Projections.

Rethinking Memorization Measures and their Implications in Large Language Models Characterizing Intrinsic Compositionality in Transformers with Tree Projections

Reference 27

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This paper cites Transformers Learn Shortcuts to Automata.

Rethinking Memorization Measures and their Implications in Large Language Models Transformers Learn Shortcuts to Automata

Reference 28

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This paper cites Learning bounded context- free-grammar via lstm and the transformer: difference and the explanations.

Rethinking Memorization Measures and their Implications in Large Language Models Learning bounded context- free-grammar via lstm and the transformer: difference and the explanations

Reference 29

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This paper cites On the Ability and Limitations of Transformers to Recognize Formal Languages.

Rethinking Memorization Measures and their Implications in Large Language Models On the Ability and Limitations of Transformers to Recognize Formal Languages

Reference 30

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Rethinking Memorization Measures and their Implications in Large Language Models Formal languages and the nlp black box

Reference 31

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This paper cites What Formal Languages Can Transformers Express? A Survey.

Rethinking Memorization Measures and their Implications in Large Language Models What Formal Languages Can Transformers Express? A Survey

Reference 32

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Rethinking Memorization Measures and their Implications in Large Language Models Theoretical limitations of self-attention in neural sequence models

Reference 33

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Rethinking Memorization Measures and their Implications in Large Language Models Neural Networks and the Chomsky Hierarchy

Reference 34

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This paper cites Why are Sensitive Functions Hard for Transformers?.

Rethinking Memorization Measures and their Implications in Large Language Models Why are Sensitive Functions Hard for Transformers?

Reference 35

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This paper cites Are All Languages Equally Hard to Language-Model?.

Rethinking Memorization Measures and their Implications in Large Language Models Are All Languages Equally Hard to Language-Model?

Reference 36

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Rethinking Memorization Measures and their Implications in Large Language Models What Kind of Language Is Hard to Language-Model?

Reference 37

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Rethinking Memorization Measures and their Implications in Large Language Models Elements of information theory

Reference 38

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Rethinking Memorization Measures and their Implications in Large Language Models Carrasco

Reference 39

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Rethinking Memorization Measures and their Implications in Large Language Models Mistral 7B

Reference 40

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This paper cites The Llama 3 Herd of Models.

Rethinking Memorization Measures and their Implications in Large Language Models The Llama 3 Herd of Models

Reference 41

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Rethinking Memorization Measures and their Implications in Large Language Models Qwen2.5 Technical Report

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Rethinking Memorization Measures and their Implications in Large Language Models Gemma: Open Models Based on Gemini Research and Technology

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This paper cites Pythia: A suite for analyzing large language models across training and scaling.

Rethinking Memorization Measures and their Implications in Large Language Models Pythia: A suite for analyzing large language models across training and scaling

Reference 44

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Rethinking Memorization Measures and their Implications in Large Language Models OPT: Open Pre-trained Transformer Language Models

Reference 45

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Rethinking Memorization Measures and their Implications in Large Language Models Cross-entropy loss functions: Theoretical analysis and applications

Reference 46

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This paper cites Are Large Pre-Trained Language Models Leaking Your Personal Information?.

Rethinking Memorization Measures and their Implications in Large Language Models Are Large Pre-Trained Language Models Leaking Your Personal Information?

Reference 47

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Rethinking Memorization Measures and their Implications in Large Language Models Propile: Probing privacy leakage in large language models

Reference 48

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This paper cites Measuring Forgetting of Memorized Training Examples.

Rethinking Memorization Measures and their Implications in Large Language Models Measuring Forgetting of Memorized Training Examples

Reference 49

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Rethinking Memorization Measures and their Implications in Large Language Models The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 50

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Rethinking Memorization Measures and their Implications in Large Language Models OLMo: Accelerating the Science of Language Models

Reference 51

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This paper cites Revisiting privacy, utility, and efficiency trade-offs when fine-tuning large language models.

Rethinking Memorization Measures and their Implications in Large Language Models Revisiting privacy, utility, and efficiency trade-offs when fine-tuning large language models

Reference 52

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This paper cites Foundation models and fair use.

Rethinking Memorization Measures and their Implications in Large Language Models Foundation models and fair use

Reference 53

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This paper cites Llms and memorization: On quality and specificity of copyright compliance.

Rethinking Memorization Measures and their Implications in Large Language Models Llms and memorization: On quality and specificity of copyright compliance

Reference 54

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Observation a9f22345-5b8b-491a-8d38-152e2a9084e0 · outbound

This paper cites Exploring Memorization and Copyright Violation in Frontier LLMs: A Study of the New York Times v. OpenAI 2023 Lawsuit.

Rethinking Memorization Measures and their Implications in Large Language Models Exploring Memorization and Copyright Violation in Frontier LLMs: A Study of the New York Times v. OpenAI 2023 Lawsuit

Reference 55

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This paper cites Undesirable memorization in large language models: A survey.

Rethinking Memorization Measures and their Implications in Large Language Models Undesirable memorization in large language models: A survey

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Observation b2f237b6-4786-4fcf-88f1-7f812050fe23 · outbound

This paper cites Measuring memorization in RLHF for code completion.

Rethinking Memorization Measures and their Implications in Large Language Models Measuring memorization in RLHF for code completion

Reference 57

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This paper cites What neural networks memorize and why: Discovering the long tail via influence estimation.

Rethinking Memorization Measures and their Implications in Large Language Models What neural networks memorize and why: Discovering the long tail via influence estimation

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:54:38.161158Z

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Observation 6885741a-8c18-4186-bead-c65f7f6f44a1 · outbound

This paper cites Understanding Transformer Memorization Recall Through Idioms.

Rethinking Memorization Measures and their Implications in Large Language Models Understanding Transformer Memorization Recall Through Idioms

Reference 59

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This paper cites Generalization v.s. Memorization: Tracing Language Models' Capabilities Back to Pretraining Data.

Rethinking Memorization Measures and their Implications in Large Language Models Generalization v.s. Memorization: Tracing Language Models' Capabilities Back to Pretraining Data

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T15:54:35.942487Z

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Observation 1fc6a6d9-70d9-4f89-b81f-79d9629410bb · outbound

This paper cites Neuron-Level Differentiation of Memorization and Generalization in Large Language Models.

Rethinking Memorization Measures and their Implications in Large Language Models Neuron-Level Differentiation of Memorization and Generalization in Large Language Models

Reference 61

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:54:37.096373Z

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Observation 084e2ae6-1562-473d-a9f9-7fd738f0bfc4 · outbound

This paper cites Mem- orize or generalize? evaluating llm code generation with evolved questions.

Rethinking Memorization Measures and their Implications in Large Language Models Mem- orize or generalize? evaluating llm code generation with evolved questions

Reference 62

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This paper cites Rethinking memorization in llms: On learning by rote vs.

Rethinking Memorization Measures and their Implications in Large Language Models Rethinking memorization in llms: On learning by rote vs

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:54:38.142821Z

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Observation 9b23b898-db20-48ec-b167-74c318f44a07 · outbound

This paper cites Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Models.

Rethinking Memorization Measures and their Implications in Large Language Models Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Models

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T15:54:36.269021Z

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Observation ca744455-129d-400e-a611-bc8905256784 · outbound

This paper cites How much do language models copy from their training data? evaluating linguistic novelty in text generation using raven.

Rethinking Memorization Measures and their Implications in Large Language Models How much do language models copy from their training data? evaluating linguistic novelty in text generation using raven

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:54:38.126751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7f1046a2-ca4f-4b21-bf5c-0da81377d50a · outbound

This paper cites Probabilistic context-free grammars (pcfgs).

Rethinking Memorization Measures and their Implications in Large Language Models Probabilistic context-free grammars (pcfgs)

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:54:38.110569Z

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Observation 3a810597-8292-4537-8cb0-b2c58caeb65a · outbound

This paper cites Three models for the description of language.IRE Transactions on information theory, 2(3):113–124, 1956.

Rethinking Memorization Measures and their Implications in Large Language Models Three models for the description of language.IRE Transactions on information theory, 2(3):113–124, 1956

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:54:38.094870Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 57a0bd6c-e27e-4b69-bcd7-040a214e93b2 · outbound

This paper cites start" or.

Rethinking Memorization Measures and their Implications in Large Language Models start" or

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:54:38.079569Z

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