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Paper Citation Record · LEDGER

Rethinking Machine Unlearning for Large Language Models

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 45 inbound Pith citation observations for arXiv:2402.08787.

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

pith.paper-citation-record.v1
2402.08787 v6

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measured 0 of 0 reference resolution

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measured 45 of 45 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 45 of 45 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:51:30.180659Z

measured 0 of 1 external citation measurements

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Source: arxiv_reference, observed 2026-07-04T14:09:53.086273Z

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Outbound references

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Pith citing papers

Observation 50e28d32-0a46-46fb-aa52-3a339740de21 · inbound

Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning cites this paper.

Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning Rethinking Machine Unlearning for Large Language Models

Reference 14

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arxiv_id, observed 2026-05-16T22:26:55.046262Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 6cda715d-932d-4854-ba4e-75e82e7bf2a5 · inbound

Does Unlearning Truly Unlearn? A Black Box Evaluation of LLM Unlearning Methods cites this paper.

Does Unlearning Truly Unlearn? A Black Box Evaluation of LLM Unlearning Methods Rethinking Machine Unlearning for Large Language Models

Reference 6

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Observation 64cbfa99-d5cf-4c25-9d76-2221eddbe8b1 · inbound

On the Impact of Fine-Tuning on Chain-of-Thought Reasoning cites this paper.

On the Impact of Fine-Tuning on Chain-of-Thought Reasoning Rethinking Machine Unlearning for Large Language Models

Reference 20

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Observation 1b61839b-4d5f-44e7-96ba-3ea293755980 · inbound

Towards Robust Evaluation of Unlearning in LLMs via Data Transformations cites this paper.

Towards Robust Evaluation of Unlearning in LLMs via Data Transformations Rethinking Machine Unlearning for Large Language Models

Reference 23

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Observation ac07f6ba-934b-464b-9584-51ee7c22bccc · inbound

From Machine Learning to Machine Unlearning: Complying with GDPR's Right to be Forgotten while Maintaining Business Value of Predictive Models cites this paper.

From Machine Learning to Machine Unlearning: Complying with GDPR's Right to be Forgotten while Maintaining Business Value of Predictive Models Rethinking Machine Unlearning for Large Language Models

Reference 42

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Observation 0d8ec195-df60-4b9e-a301-cf15269430b4 · inbound

SEUF: Is Unlearning One Expert Enough for Mixture-of-Experts LLMs? cites this paper.

SEUF: Is Unlearning One Expert Enough for Mixture-of-Experts LLMs? Rethinking Machine Unlearning for Large Language Models

Reference 37

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Observation 754b813c-a367-4d43-ab82-cee2e48374bd · inbound

Unified Parameter-Efficient Unlearning for LLMs cites this paper.

Unified Parameter-Efficient Unlearning for LLMs Rethinking Machine Unlearning for Large Language Models

Reference 53

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Observation 9058515f-f44d-4285-950e-24fae4c36e92 · inbound

Learning to Forget using Hypernetworks cites this paper.

Learning to Forget using Hypernetworks Rethinking Machine Unlearning for Large Language Models

Reference 28

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Observation fe35c3a6-2a8b-4786-bf4d-1bcb337d3aa7 · inbound

Copyright-Protected Language Generation via Adaptive Model Fusion cites this paper.

Copyright-Protected Language Generation via Adaptive Model Fusion Rethinking Machine Unlearning for Large Language Models

Reference 44

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Observation 0424f23f-77b2-42d5-af59-978f70668b71 · inbound

Political-LLM: Large Language Models in Political Science cites this paper.

Political-LLM: Large Language Models in Political Science Rethinking Machine Unlearning for Large Language Models

Reference 63

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Observation ff638570-73b1-43fb-8d68-257702baefe4 · inbound

Underestimated Privacy Risks for Minority Populations in Large Language Model Unlearning cites this paper.

Underestimated Privacy Risks for Minority Populations in Large Language Model Unlearning Rethinking Machine Unlearning for Large Language Models

Reference 34

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Observation 327580e7-1e70-4c6a-8392-93f85def5b65 · inbound

Forget Vectors at Play: Universal Input Perturbations Driving Machine Unlearning in Image Classification cites this paper.

Forget Vectors at Play: Universal Input Perturbations Driving Machine Unlearning in Image Classification Rethinking Machine Unlearning for Large Language Models

Reference 16

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Observation d148ccbc-3443-467b-ae44-7209132950ff · inbound

Multi-Objective Large Language Model Unlearning cites this paper.

Multi-Objective Large Language Model Unlearning Rethinking Machine Unlearning for Large Language Models

Reference 7

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Observation 368d352c-4ff3-4ce1-a49f-fc72ef9dbe6a · inbound

(De)-Indexing and the Right to be Forgotten cites this paper.

(De)-Indexing and the Right to be Forgotten Rethinking Machine Unlearning for Large Language Models

Reference 18

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Observation 7f2c20d6-36f8-41b4-bf61-8c61cea2a639 · inbound

Open Problems in Machine Unlearning for AI Safety cites this paper.

Open Problems in Machine Unlearning for AI Safety Rethinking Machine Unlearning for Large Language Models

Reference 78

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Observation 43fa941f-bc85-45fa-9306-d71125a8692c · inbound

Resolving Editing-Unlearning Conflicts: A Knowledge Codebook Framework for Large Language Model Updating cites this paper.

Resolving Editing-Unlearning Conflicts: A Knowledge Codebook Framework for Large Language Model Updating Rethinking Machine Unlearning for Large Language Models

Reference 43

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source=arxiv_source observed=2026-08-09T20:04:50.089746Z digest=sha256:2f8896e9c76d4ae5c25431154ca20d72860e5f7761297bff8a5ed8f7f1b0334a

Observation 9a67d7ca-f77f-4652-8175-fc201462abfa · inbound

Model Tampering Attacks Enable More Rigorous Evaluations of LLM Capabilities cites this paper.

Model Tampering Attacks Enable More Rigorous Evaluations of LLM Capabilities Rethinking Machine Unlearning for Large Language Models

Reference 42

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Observation 4b8c5de1-8909-4e67-8d58-6f9a875dd5e6 · inbound

A Survey of Theory of Mind in Large Language Models: Evaluations, Representations, and Safety Risks cites this paper.

A Survey of Theory of Mind in Large Language Models: Evaluations, Representations, and Safety Risks Rethinking Machine Unlearning for Large Language Models

Reference 28

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Observation 9eaf6612-b391-464a-99a2-ef01f4b73d78 · inbound

Knowledge Swapping via Learning and Unlearning cites this paper.

Knowledge Swapping via Learning and Unlearning Rethinking Machine Unlearning for Large Language Models

Reference 2022

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Observation 5e39d783-26b0-4e81-8316-a35eae8c053f · inbound

EnronQA: Towards Personalized RAG over Private Documents cites this paper.

EnronQA: Towards Personalized RAG over Private Documents Rethinking Machine Unlearning for Large Language Models

Reference 45

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Observation 8f5ca02b-b10f-4a8a-96ea-bfebf19b68d5 · inbound

Automatic Calibration for Membership Inference Attack on Large Language Models cites this paper.

Automatic Calibration for Membership Inference Attack on Large Language Models Rethinking Machine Unlearning for Large Language Models

Reference 14

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Observation 611368a1-0103-4d3d-b31c-d024d62a7770 · inbound

OBLIVIATE: Robust and Practical Machine Unlearning for Large Language Models cites this paper.

OBLIVIATE: Robust and Practical Machine Unlearning for Large Language Models Rethinking Machine Unlearning for Large Language Models

Reference 34

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Observation 81f69686-9de3-498a-a770-236df717e67a · inbound

Retrieval Augmented Generation Evaluation for Health Documents cites this paper.

Retrieval Augmented Generation Evaluation for Health Documents Rethinking Machine Unlearning for Large Language Models

Reference 25

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Observation c0bff03e-a51f-464f-b596-aaaf0660d4df · inbound

Do LLMs Memorize Recommendation Datasets? A Preliminary Study on MovieLens-1M cites this paper.

Do LLMs Memorize Recommendation Datasets? A Preliminary Study on MovieLens-1M Rethinking Machine Unlearning for Large Language Models

Reference 20

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Observation f51eee70-a9c4-4760-9381-122a9066d5b0 · inbound

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning cites this paper.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Rethinking Machine Unlearning for Large Language Models

Reference 41

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source=arxiv_source observed=2026-08-15T20:48:52.177693Z digest=sha256:71797f08f7307d8490b8f5106c5e1ba2bdb53e65880b2969f097a56c166d7e6d

Observation d39c5397-f114-4499-8a31-5338f1812098 · inbound

Hey, That's My Data! Token-Only Dataset Inference in Large Language Models cites this paper.

Hey, That's My Data! Token-Only Dataset Inference in Large Language Models Rethinking Machine Unlearning for Large Language Models

Reference 23

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Observation 5be0cd80-0b1d-4ffa-b66e-88c4344deaff · inbound

Certified Unlearning for Neural Networks cites this paper.

Certified Unlearning for Neural Networks Rethinking Machine Unlearning for Large Language Models

Reference 26

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Observation 99ad6a54-a033-4754-88df-451a54ab7851 · inbound

LLM Unlearning Should Be Form-Independent cites this paper.

LLM Unlearning Should Be Form-Independent Rethinking Machine Unlearning for Large Language Models

Reference 5

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Observation a09cf349-7d28-4003-9aff-ae26eaf6f772 · inbound

SoK: Machine Unlearning for Large Language Models cites this paper.

SoK: Machine Unlearning for Large Language Models Rethinking Machine Unlearning for Large Language Models

Reference 10

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Observation 2f4d074e-f227-4423-b215-87df55431d3a · inbound

SOFT: Selective Data Obfuscation for Protecting LLM Fine-tuning against Membership Inference Attacks cites this paper.

SOFT: Selective Data Obfuscation for Protecting LLM Fine-tuning against Membership Inference Attacks Rethinking Machine Unlearning for Large Language Models

Reference 55

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source=pdf_text observed=2026-08-07T04:33:16.940137Z digest=sha256:5a3ad4f74b9bd0c6e9ed021ad448d83092e6dfaa53cc340547c90e6a77329a03

Observation 535be7b8-3d09-4be7-9965-7f2252555238 · inbound

Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models cites this paper.

Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models Rethinking Machine Unlearning for Large Language Models

Reference 20

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Observation d911cbf0-64e7-4088-87b8-f12be1720d27 · inbound

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests cites this paper.

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests Rethinking Machine Unlearning for Large Language Models

Reference 33

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source=pdf_text observed=2026-08-06T17:21:33.653771Z digest=sha256:f09601ae23075e7de8c1dfa2e737c7d3eb9b3d9117e731318ca9ecd5e14f3e61

Observation 22353f34-2e71-44d0-a5e3-e8260d35e8f6 · inbound

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design cites this paper.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Rethinking Machine Unlearning for Large Language Models

Reference 4

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Observation 8e57d700-4c93-44b8-b725-93716675060d · inbound

Module-Aware Parameter-Efficient Machine Unlearning on Transformers cites this paper.

Module-Aware Parameter-Efficient Machine Unlearning on Transformers Rethinking Machine Unlearning for Large Language Models

Reference 32

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source=pdf_text observed=2026-08-05T17:06:12.054895Z digest=sha256:554255d0cc59574ed33cc9a9edfa49d14cde05f0de7e1755a867930fef572c5f

Observation 428ebb10-1885-4eeb-ac3d-10b997531766 · inbound

EEPO: Exploration-Enhanced Policy Optimization via Sample-Then-Forget cites this paper.

EEPO: Exploration-Enhanced Policy Optimization via Sample-Then-Forget Rethinking Machine Unlearning for Large Language Models

Reference 10

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arxiv_id, observed 2026-05-18T08:56:08.710406Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 625a3723-80ab-4770-91e5-7c76d3b49858 · inbound

A Mechanistic Perspective and Circuit-Guided Difficulty Metric for Unlearning cites this paper.

A Mechanistic Perspective and Circuit-Guided Difficulty Metric for Unlearning Rethinking Machine Unlearning for Large Language Models

Reference 33

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source=arxiv_source observed=2026-08-03T10:37:01.955321Z digest=sha256:4e0d452e15a6859297a3847efca2f5ae63b3b0589d6ae0fffc2c83edb3309ee7

Observation 6d8b3afb-3881-48f5-bd33-85475fcb3c55 · inbound

Representation-Guided Parameter-Efficient LLM Unlearning cites this paper.

Representation-Guided Parameter-Efficient LLM Unlearning Rethinking Machine Unlearning for Large Language Models

Reference 150

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arxiv_id, observed 2026-05-10T06:06:19.213622Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-05-10T06:01:46.885030Z digest=sha256:38755b67e05b1bc6466f7e78b17405ed6da6ba3ef39e70bb3b79b787bd03ac0c

Observation eeadee5a-7e13-44c2-a440-688dff4375f9 · inbound

Model Unlearning Objectives Vary for Distinct Language Functions cites this paper.

Model Unlearning Objectives Vary for Distinct Language Functions Rethinking Machine Unlearning for Large Language Models

Reference 5

Resolution
metadata mismatch
arxiv_id, observed 2026-06-29T18:43:50.789291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation fd323305-a778-437b-bdb4-bb0392a572eb · inbound

TRACER: Token ReAssignment for Concept ERasure in Generative Recommendation cites this paper.

TRACER: Token ReAssignment for Concept ERasure in Generative Recommendation Rethinking Machine Unlearning for Large Language Models

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-07-02T20:07:21.205212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-06-27T21:04:06.408281Z digest=sha256:716bc2df919130135770d046f7748736d528a249322d842fa7cacff799d1ef1a

Observation 84ea8f7a-581a-4011-b8ea-915e215c93b2 · inbound

RepSelect: Robust LLM Unlearning via Representation Selectivity cites this paper.

RepSelect: Robust LLM Unlearning via Representation Selectivity Rethinking Machine Unlearning for Large Language Models

Reference 25

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T17:58:47.212712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-06-27T03:34:32.388152Z digest=sha256:6c0641383177e2e743676ec3f7b88a0eb3027288d39c046e1e7d7964887edb6b

Observation 486c8d15-0ffb-47b5-8b3e-eecb406617b3 · inbound

Agents That Know Too Much: A Data-Centric Survey of Privacy in LLM Agents cites this paper.

Agents That Know Too Much: A Data-Centric Survey of Privacy in LLM Agents Rethinking Machine Unlearning for Large Language Models

Reference 74

Resolution
verified exact
arxiv_id, observed 2026-07-04T14:09:53.088017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-06-26T04:29:16.386339Z digest=sha256:e497b47c8631b4a43a87103fa2a9f8550761be6bd67ee7d367b141f411043bf8

Observation c51865bc-c2e9-4872-9020-5b5fe8f91b92 · inbound

MPSelectTune: Prompt-type Selection for Fine-tuning improves Concept Unlearning in LLMs cites this paper.

MPSelectTune: Prompt-type Selection for Fine-tuning improves Concept Unlearning in LLMs Rethinking Machine Unlearning for Large Language Models

Reference 8

Resolution
unresolved
no resolver link, observed 2026-07-11T22:55:43.595779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-11T22:55:43.595779Z digest=sha256:f6649b469ad721201b2d4687ade31bf1be86b27c6e1d4815637493b12f361e8e

Observation 1275c440-a1b5-44ca-bdf6-4319f768bf74 · inbound

Understanding Machine Unlearning Through the Lens of Mode Connectivity cites this paper.

Understanding Machine Unlearning Through the Lens of Mode Connectivity Rethinking Machine Unlearning for Large Language Models

Reference 45

Resolution
unresolved
no resolver link, observed 2026-07-31T23:27:27.763426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-31T23:27:27.763426Z digest=sha256:113de4ce4a6e2d100efd2cbeaf23673253415f32c82f73267ffa2fd96bc847e9

Observation b05815a3-b43e-447e-a4e0-7ce922c7ae1b · inbound

Policy-Masked Private Experts: Auditable and Reversible Capability Access Control in Sparse MoE Models cites this paper.

Policy-Masked Private Experts: Auditable and Reversible Capability Access Control in Sparse MoE Models Rethinking Machine Unlearning for Large Language Models

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-10T22:29:38.760764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:29:38.760764Z digest=sha256:5728fc21ddfacb93477f7495e01abd553d304727c10d0938ef2ef2b47bf88007

Observation 79986ac4-570c-404d-a6df-4429f6682356 · inbound

MOON: Multi-Objective OrthoNormalized Updates for Multitask Learning cites this paper.

MOON: Multi-Objective OrthoNormalized Updates for Multitask Learning Rethinking Machine Unlearning for Large Language Models

Reference 185

Resolution
unresolved
no resolver link, observed 2026-08-16T00:36:02.574517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T00:36:02.574517Z digest=sha256:5492b60b28a9bac18fdcfe9e8b5067a4faad9f5d9423be843a672b93cfb6e066