Paper Citation Record · LEDGER

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

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 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 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:33:16.857746Z

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 2026-08-07T06:34:17.273281+00:00.

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.