Paper Citation Record · LEDGER

Learning and Forgetting Unsafe Examples in Large Language Models

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2312.12736.

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

pith.paper-citation-record.v1
2312.12736 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:57:58.403124Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T17:51:08.518907Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 17bd6627-4843-46e6-960b-24752bc1c028 · inbound

Separate the Wheat from the Chaff: A Post-Hoc Approach to Safety Re-Alignment for Fine-Tuned Language Models cites this paper.

Separate the Wheat from the Chaff: A Post-Hoc Approach to Safety Re-Alignment for Fine-Tuned Language Models Learning and Forgetting Unsafe Examples in Large Language Models

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-11T15:26:52.654464Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3d6893a-b1a3-42d2-8783-f956295b4005 · inbound

Fun-tuning: Characterizing the Vulnerability of Proprietary LLMs to Optimization-based Prompt Injection Attacks via the Fine-Tuning Interface cites this paper.

Fun-tuning: Characterizing the Vulnerability of Proprietary LLMs to Optimization-based Prompt Injection Attacks via the Fine-Tuning Interface Learning and Forgetting Unsafe Examples in Large Language Models

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-10T19:44:46.862115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 328eb7db-aa73-4019-a1bb-2600609ff86d · inbound

A Data-Centric Approach for Safe and Secure Large Language Models against Threatening and Toxic Content cites this paper.

A Data-Centric Approach for Safe and Secure Large Language Models against Threatening and Toxic Content Learning and Forgetting Unsafe Examples in Large Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T11:57:58.403124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8468419-36bd-4c0e-a9d5-566db44ef6cd · inbound

Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives cites this paper.

Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives Learning and Forgetting Unsafe Examples in Large Language Models

Reference 108

Resolution: unresolved; no resolver link, observed 2026-08-07T04:46:04.431963Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d12a7817-57a1-460c-95f7-ee4881b46508 · inbound

Poison Once, Refuse Forever: Weaponizing Alignment for Injecting Bias in LLMs cites this paper.

Poison Once, Refuse Forever: Weaponizing Alignment for Injecting Bias in LLMs Learning and Forgetting Unsafe Examples in Large Language Models

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-15T16:51:54.975579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eaa77cbf-6a9d-4e63-a703-728aa3374c24 · inbound

You Snooze, You Lose: Automatic Safety Alignment Restoration through Neural Weight Translation cites this paper.

You Snooze, You Lose: Automatic Safety Alignment Restoration through Neural Weight Translation Learning and Forgetting Unsafe Examples in Large Language Models

Reference 150

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:51:08.520740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.