Paper Citation Record · LEDGER

Towards Evaluation for Real-World LLM Unlearning

As of 18 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2508.01324.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2508.01324 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:48:02.907156Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

46 of 46 outbound references displayed

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External citation measurements

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Outbound references

Observation 5db9453e-bb15-42e4-888c-32fcae6c3ed6 · outbound

This paper cites Sulla determinazione empirica di una legge didistribuzione.

Towards Evaluation for Real-World LLM Unlearning Sulla determinazione empirica di una legge didistribuzione

Reference 1

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This paper cites Machine unlearning.

Towards Evaluation for Real-World LLM Unlearning Machine unlearning

Reference 2

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This paper cites Extracting training data from large language models.

Towards Evaluation for Real-World LLM Unlearning Extracting training data from large language models

Reference 3

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Observation d8baadf5-d4a0-44f1-8adc-2e4bd587d0f0 · outbound

This paper cites Unlearn What You Want to Forget: Efficient Unlearning for LLMs.

Towards Evaluation for Real-World LLM Unlearning Unlearn What You Want to Forget: Efficient Unlearning for LLMs

Reference 4

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Observation 7e6c7a0d-a9dc-455d-b2b3-ad61c5568607 · outbound

This paper cites Simplicity prevails: Rethinking negative preference optimization for llm unlearning, 2024.

Towards Evaluation for Real-World LLM Unlearning Simplicity prevails: Rethinking negative preference optimization for llm unlearning, 2024

Reference 5

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This paper cites LawBench: Benchmarking Legal Knowledge of Large Language Models.

Towards Evaluation for Real-World LLM Unlearning LawBench: Benchmarking Legal Knowledge of Large Language Models

Reference 6

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Observation 414fd268-5811-4931-9a20-c655fd4f282f · outbound

This paper cites Ticketed learning–unlearning schemes.

Towards Evaluation for Real-World LLM Unlearning Ticketed learning–unlearning schemes

Reference 7

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Observation 4dace75a-b854-4480-ad13-d80aaf9291e1 · outbound

This paper cites Making ai forget you: Data deletion in machine learning.

Towards Evaluation for Real-World LLM Unlearning Making ai forget you: Data deletion in machine learning

Reference 8

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This paper cites Adaptive machine unlearning.

Towards Evaluation for Real-World LLM Unlearning Adaptive machine unlearning

Reference 9

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Towards Evaluation for Real-World LLM Unlearning Fantastic Copyrighted Beasts and How (Not) to Generate Them

Reference 10

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Observation 931bf2cb-d779-4145-b4f9-d0b09616ac81 · outbound

This paper cites Foundation Models and Fair Use.

Towards Evaluation for Real-World LLM Unlearning Foundation Models and Fair Use

Reference 11

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This paper cites Unlearning or Obfuscating? Jogging the Memory of Unlearned LLMs via Benign Relearning.

Towards Evaluation for Real-World LLM Unlearning Unlearning or Obfuscating? Jogging the Memory of Unlearned LLMs via Benign Relearning

Reference 12

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Observation c7211c40-63de-445f-997a-d3c405bfb183 · outbound

This paper cites Eraser: Machine unlearning in mlaas via an inference serving-aware approach.

Towards Evaluation for Real-World LLM Unlearning Eraser: Machine unlearning in mlaas via an inference serving-aware approach

Reference 13

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This paper cites Editing models with task arithmetic, 2023.

Towards Evaluation for Real-World LLM Unlearning Editing models with task arithmetic, 2023

Reference 14

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Towards Evaluation for Real-World LLM Unlearning Knowledge sanitization of large language models,

Reference 15

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This paper cites Approximate data deletion from machine learning models.

Towards Evaluation for Real-World LLM Unlearning Approximate data deletion from machine learning models

Reference 16

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Observation 516c5bd1-6858-49d0-82e0-5c232f8ae232 · outbound

This paper cites Knowledge Unlearning for Mitigating Privacy Risks in Language Models.

Towards Evaluation for Real-World LLM Unlearning Knowledge Unlearning for Mitigating Privacy Risks in Language Models

Reference 17

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Observation f5c278cd-5460-4480-a3e7-98ac5ede2096 · outbound

This paper cites RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models.

Towards Evaluation for Real-World LLM Unlearning RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models

Reference 18

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This paper cites Privacy Adhering Machine Un-learning in NLP.

Towards Evaluation for Real-World LLM Unlearning Privacy Adhering Machine Un-learning in NLP

Reference 19

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This paper cites The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning.

Towards Evaluation for Real-World LLM Unlearning The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning

Reference 20

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This paper cites Large language models in finance: A survey.

Towards Evaluation for Real-World LLM Unlearning Large language models in finance: A survey

Reference 21

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Towards Evaluation for Real-World LLM Unlearning Continual learning and private unlearning, 2022

Reference 22

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Towards Evaluation for Real-World LLM Unlearning Federated Unlearning

Reference 23

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Towards Evaluation for Real-World LLM Unlearning Quark: Controllable text generation with reinforced unlearning

Reference 24

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This paper cites Eight methods to evaluate robust unlearning in llms, 2024.

Towards Evaluation for Real-World LLM Unlearning Eight methods to evaluate robust unlearning in llms, 2024

Reference 25

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Observation a9ac0ed7-b71a-4457-a1d9-95ff66c7ab1d · outbound

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Towards Evaluation for Real-World LLM Unlearning TOFU: A Task of Fictitious Unlearning for LLMs

Reference 26

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Towards Evaluation for Real-World LLM Unlearning SILO Language Models: Isolating Legal Risk In a Nonparametric Datastore

Reference 27

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Towards Evaluation for Real-World LLM Unlearning Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks

Reference 28

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Towards Evaluation for Real-World LLM Unlearning In-Context Unlearning: Language Models as Few Shot Unlearners

Reference 29

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Towards Evaluation for Real-World LLM Unlearning Manning, and Chelsea Finn

Reference 30

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This paper cites Remember what you want to forget: Algorithms for machine unlearning.

Towards Evaluation for Real-World LLM Unlearning Remember what you want to forget: Algorithms for machine unlearning

Reference 31

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This paper cites MUSE: Machine Unlearning Six-Way Evaluation for Language Models.

Towards Evaluation for Real-World LLM Unlearning MUSE: Machine Unlearning Six-Way Evaluation for Language Models

Reference 32

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Towards Evaluation for Real-World LLM Unlearning Unresolved cited work

Reference 33

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Towards Evaluation for Real-World LLM Unlearning Chatgpt: Optimizing language models for dialogue

Reference 34

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This paper cites Guardrail baselines for unlearning in llms, 2024.

Towards Evaluation for Real-World LLM Unlearning Guardrail baselines for unlearning in llms, 2024

Reference 35

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Towards Evaluation for Real-World LLM Unlearning Large language models in medicine.Nature medicine, 29(8):1930–1940, 2023

Reference 36

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Towards Evaluation for Real-World LLM Unlearning Towards efficient and effective unlearning of large language models for recommendation,

Reference 37

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Towards Evaluation for Real-World LLM Unlearning DEPN: Detecting and Editing Privacy Neurons in Pretrained Language Models

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Towards Evaluation for Real-World LLM Unlearning Towards Efficient and Effective Unlearning of Large Language Models for Recommendation

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Towards Evaluation for Real-World LLM Unlearning Evaluating reading comprehension exercises generated by llms: A showcase of chatgpt in education applications

Reference 40

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Towards Evaluation for Real-World LLM Unlearning Deltagrad: Rapid retraining of machine learning models

Reference 41

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Towards Evaluation for Real-World LLM Unlearning Composing Parameter-Efficient Modules with Arithmetic Operations

Reference 42

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Towards Evaluation for Real-World LLM Unlearning Unlearning bias in language models by partitioning gradients

Reference 43

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Towards Evaluation for Real-World LLM Unlearning Catastrophic Failure of LLM Unlearning via Quantization

Reference 44

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Towards Evaluation for Real-World LLM Unlearning Negative preference optimization: From catastrophic collapse to effective unlearning, 2024

Reference 45

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Towards Evaluation for Real-World LLM Unlearning Knowledge Sanitization of Large Language Models

Reference 2024

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Pith citing papers

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