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Paper Citation Record · LEDGER

Multi-Objective Large Language Model Unlearning

As of 14 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2412.20412.

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

pith.paper-citation-record.v1
2412.20412 v2

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:27:37.796703Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

31 of 31 outbound references displayed

  • verified exact0
  • verified fuzzy11
  • unresolved20
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 33a9d9b1-5a6b-4f47-a40b-b6bfb7d47ebb · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

Multi-Objective Large Language Model Unlearning Constitutional AI: Harmlessness from AI Feedback

Reference 1

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Source-reported events for the cited work

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Observation f1ffd72f-fce4-4248-a380-063a20d7dd5e · outbound

This paper cites Copyright Traps for Large Language Models.

Multi-Objective Large Language Model Unlearning Copyright Traps for Large Language Models

Reference 2

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no resolver link, observed 2026-08-10T23:27:37.646725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 3e49c09b-c82f-4413-a0b9-b7be2953538f · outbound

This paper cites Towards mitigating llm hallucination via self reflection,.

Multi-Objective Large Language Model Unlearning Towards mitigating llm hallucination via self reflection,

Reference 3

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 7d990619-5a40-459e-8767-3256fb66e877 · outbound

This paper cites Privacy and data protection in chatgpt and other ai chatbots: strategies for securing user information,.

Multi-Objective Large Language Model Unlearning Privacy and data protection in chatgpt and other ai chatbots: strategies for securing user information,

Reference 4

Resolution
verified fuzzy
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Source-reported events for the cited work

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Observation 7bbdb462-4e5b-450f-95c6-2c7ec7584ac8 · outbound

This paper cites Firewallm: A portable data protection and recovery framework for llm services,.

Multi-Objective Large Language Model Unlearning Firewallm: A portable data protection and recovery framework for llm services,

Reference 5

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation fbdfaf40-281d-471f-9a68-54eebda3aaa2 · outbound

This paper cites Large Language Model Unlearning.

Multi-Objective Large Language Model Unlearning Large Language Model Unlearning

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

This paper cites Rethinking Machine Unlearning for Large Language Models.

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

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 46abda67-5e2d-471c-b194-3a98f6ac28cf · outbound

This paper cites Efuf: Efficient fine-grained unlearning framework for mitigating hallucinations in multimodal large language models,.

Multi-Objective Large Language Model Unlearning Efuf: Efficient fine-grained unlearning framework for mitigating hallucinations in multimodal large language models,

Reference 8

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation ca05832e-3197-4170-8ffb-9cdac1e532ae · outbound

This paper cites Avoiding Copyright Infringement via Large Language Model Unlearning.

Multi-Objective Large Language Model Unlearning Avoiding Copyright Infringement via Large Language Model Unlearning

Reference 9

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unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 70caa778-1a13-459f-aa3d-ef593e15192f · outbound

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

Multi-Objective Large Language Model Unlearning Unlearn What You Want to Forget: Efficient Unlearning for LLMs

Reference 10

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 6c604a7d-d661-4455-897b-13ff31d233a5 · outbound

This paper cites In-Context Unlearning: Language Models as Few Shot Unlearners.

Multi-Objective Large Language Model Unlearning In-Context Unlearning: Language Models as Few Shot Unlearners

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 7b462e9e-fec7-4ab0-b0ec-9522e0ae1d77 · outbound

This paper cites Formalizing and bench- marking prompt injection attacks and defenses,.

Multi-Objective Large Language Model Unlearning Formalizing and bench- marking prompt injection attacks and defenses,

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation c8357770-320c-4aa5-b8b7-a79f64ef382b · outbound

This paper cites Who's Harry Potter? Approximate Unlearning in LLMs.

Multi-Objective Large Language Model Unlearning Who's Harry Potter? Approximate Unlearning in LLMs

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 36e53f8e-b412-4ad6-94b0-e8ff173949b5 · outbound

This paper cites Training language models to fol- low instructions with human feedback,.

Multi-Objective Large Language Model Unlearning Training language models to fol- low instructions with human feedback,

Reference 14

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation f6f314ed-ac70-4431-b3ef-87af8c9691a3 · outbound

This paper cites RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback.

Multi-Objective Large Language Model Unlearning RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback

Reference 15

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation ddc37edb-ff56-47aa-9f60-3289bcef070d · outbound

This paper cites $\nabla \tau$: Gradient-based and Task-Agnostic machine Unlearning.

Multi-Objective Large Language Model Unlearning $\nabla \tau$: Gradient-based and Task-Agnostic machine Unlearning

Reference 16

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation fb0c41cd-55c5-46a1-91a9-d91cd821d78a · outbound

This paper cites A More Practical Approach to Machine Unlearning.

Multi-Objective Large Language Model Unlearning A More Practical Approach to Machine Unlearning

Reference 17

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 1a8416db-f3a2-481e-ad0b-db1053766c1c · outbound

This paper cites Federated Unlearning: How to Efficiently Erase a Client in FL?.

Multi-Objective Large Language Model Unlearning Federated Unlearning: How to Efficiently Erase a Client in FL?

Reference 18

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unresolved
no resolver link, observed 2026-08-10T23:27:37.732824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 9a73e739-16e3-41b2-b0fe-ea773c4c1740 · outbound

This paper cites Towards Unbounded Machine Unlearning.

Multi-Objective Large Language Model Unlearning Towards Unbounded Machine Unlearning

Reference 19

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no resolver link, observed 2026-08-10T23:27:37.737596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 1c2e0654-df77-4799-972b-b57bcfff8d00 · outbound

This paper cites PKU-SafeRLHF: Towards Multi-Level Safety Alignment for LLMs with Human Preference.

Multi-Objective Large Language Model Unlearning PKU-SafeRLHF: Towards Multi-Level Safety Alignment for LLMs with Human Preference

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation c883c3c5-128a-4c4e-9036-4e0332e8e985 · outbound

This paper cites Steepest descent methods for multicriteria optimization,.

Multi-Objective Large Language Model Unlearning Steepest descent methods for multicriteria optimization,

Reference 21

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 9b226c97-0bae-4bba-b36c-1da3f7ef7169 · outbound

This paper cites Fedmdfg: Federated learning with multi-gradient descent and fair guidance,.

Multi-Objective Large Language Model Unlearning Fedmdfg: Federated learning with multi-gradient descent and fair guidance,

Reference 22

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 36791047-362a-4c63-8d8c-b66ff462d1ed · outbound

This paper cites Fedlf: Layer-wise fair federated learn- ing,.

Multi-Objective Large Language Model Unlearning Fedlf: Layer-wise fair federated learn- ing,

Reference 23

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Source-reported events for the cited work

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Observation c9a2e506-8b5f-43e5-bff3-ef368a6884b7 · outbound

This paper cites Polyhedral geometry and linear optimization,.

Multi-Objective Large Language Model Unlearning Polyhedral geometry and linear optimization,

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 113df591-b060-40c1-917d-d9be0d1ae031 · outbound

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Multi-Objective Large Language Model Unlearning Unresolved cited work

Reference 25

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Source-reported events for the cited work

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Observation 4ec4aac2-040e-4e57-a0e9-4caf45030007 · outbound

This paper cites Beavertails: Towards improved safety alignment of llm via a human-preference dataset,.

Multi-Objective Large Language Model Unlearning Beavertails: Towards improved safety alignment of llm via a human-preference dataset,

Reference 26

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 34bda88f-024d-42dc-b023-092709e02464 · outbound

This paper cites Orthogonal gradient descent for continual learning,.

Multi-Objective Large Language Model Unlearning Orthogonal gradient descent for continual learning,

Reference 27

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation af876fb0-4f54-4f0c-9f70-11b1f367ab7e · outbound

This paper cites Random Relabeling for Efficient Machine Unlearning.

Multi-Objective Large Language Model Unlearning Random Relabeling for Efficient Machine Unlearning

Reference 28

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation ffd8d084-fa4b-4ef6-bf59-c99269c30626 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Multi-Objective Large Language Model Unlearning LLaMA: Open and Efficient Foundation Language Models

Reference 29

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 915260fe-13f6-49e3-b797-6a5068a1f6aa · outbound

This paper cites Detoxify,.

Multi-Objective Large Language Model Unlearning Detoxify,

Reference 30

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 75afe604-d904-4175-8226-ea80a9e5e898 · outbound

This paper cites A contrastive framework for neural text generation,.

Multi-Objective Large Language Model Unlearning A contrastive framework for neural text generation,

Reference 31

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Pith citing papers

No inbound Pith citation observations are available.