Paper Citation Record · LEDGER

DE-COP: Detecting Copyrighted Content in Language Models Training Data

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2402.09910.

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

pith.paper-citation-record.v1
2402.09910 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 2026-08-11T06:34:44.6726+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:16:49.554741Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T23:10:40.919106Z

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 dc1de3e1-5482-43ad-810e-bfdcf621033d · inbound

Benchmark Data Contamination of Large Language Models: A Survey cites this paper.

Benchmark Data Contamination of Large Language Models: A Survey DE-COP: Detecting Copyrighted Content in Language Models Training Data

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-22T23:10:40.921901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d267e169-7eca-4d2f-9515-09e00b009aa9 · inbound

Investigating the Feasibility of Mitigating Potential Copyright Infringement via Large Language Model Unlearning cites this paper.

Investigating the Feasibility of Mitigating Potential Copyright Infringement via Large Language Model Unlearning DE-COP: Detecting Copyrighted Content in Language Models Training Data

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T14:16:49.554741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 258a3b3b-3f90-4d57-b407-59c910c9a5f5 · inbound

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs cites this paper.

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs DE-COP: Detecting Copyrighted Content in Language Models Training Data

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T23:40:18.766132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2527a9af-743b-431b-8d9a-537da256cbc9 · 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 DE-COP: Detecting Copyrighted Content in Language Models Training Data

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T04:33:16.857746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7c1395e-899e-4af7-9412-f6c2b33f51fb · inbound

Identifying Pre-training Data in LLMs: A Neuron Activation-Based Detection Framework cites this paper.

Identifying Pre-training Data in LLMs: A Neuron Activation-Based Detection Framework DE-COP: Detecting Copyrighted Content in Language Models Training Data

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T15:15:18.313771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2307587c-5946-46b2-bbdf-e91a0de37ef2 · inbound

PPE-Bench: A Benchmark for Evaluating MLLM Unlearning under Private-Public Entanglement cites this paper.

PPE-Bench: A Benchmark for Evaluating MLLM Unlearning under Private-Public Entanglement DE-COP: Detecting Copyrighted Content in Language Models Training Data

Reference 32

Resolution: unresolved; no resolver link, observed 2026-07-12T06:18:42.939955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.