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Paper Citation Record · LEDGER

Data-centric NLP Backdoor Defense from the Lens of Memorization

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2409.14200.

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

pith.paper-citation-record.v1
2409.14200 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T13:44:40.109326Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T04:42:33.832851Z

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 4833bb12-3d2f-40ad-accf-d234298e7c01 · inbound

Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety cites this paper.

Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety Data-centric NLP Backdoor Defense from the Lens of Memorization

Reference 175

Resolution
verified exact
arxiv_id, observed 2026-05-23T04:42:33.836101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-23T04:39:04.591722Z digest=sha256:7d64b580d3591045349cbe4f48e7ca6fa4e581bcf0fc31768a1d19417e936325

Observation 0150461c-3fb0-482f-9167-c942eb04b180 · inbound

Backdoor Samples Detection Based on Perturbation Discrepancy Consistency in Pre-trained Language Models cites this paper.

Backdoor Samples Detection Based on Perturbation Discrepancy Consistency in Pre-trained Language Models Data-centric NLP Backdoor Defense from the Lens of Memorization

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-05T13:44:40.109326Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:44:40.109326Z digest=sha256:368c7f2788866c1f3032a1d221bb76161342ffbbf2a209003bc752aae7d71755