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Paper Citation Record · LEDGER

Towards Improving Adversarial Training of NLP Models

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2109.00544.

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

pith.paper-citation-record.v1
2109.00544 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T23:10:26.200465Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T00:28:23.411114Z

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 e30d6c7b-9ffa-4adb-9292-1808aba96710 · inbound

Hijacking Vision-and-Language Navigation Agents with Adversarial Environmental Attacks cites this paper.

Hijacking Vision-and-Language Navigation Agents with Adversarial Environmental Attacks Towards Improving Adversarial Training of NLP Models

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-11T23:10:26.200465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:10:26.200465Z digest=sha256:91c4413c75f4009d8f5a9721075bbc20cee770669269c1a8f952a06142967895

Observation 79f74199-6f23-4c0a-9917-2c8a5331cb09 · inbound

On Adversarial Robustness of Language Models in Transfer Learning cites this paper.

On Adversarial Robustness of Language Models in Transfer Learning Towards Improving Adversarial Training of NLP Models

Reference 16

Resolution
malformed identifier
no resolver link, observed 2026-08-10T23:21:41.821380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:21:41.821380Z digest=sha256:f4ab9f21e2e7923d1d501145e57371fff71b535266a9a3cde5f8e3d72d81d55c

Observation 5f836170-2d87-4ba3-822b-bf0a40083432 · inbound

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation cites this paper.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Towards Improving Adversarial Training of NLP Models

Reference 134

Resolution
unresolved
no resolver link, observed 2026-08-07T00:46:10.490308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:46:10.490308Z digest=sha256:a4d68ae83d9dcfca4bc8a906c870d2fa78a638f3ca6e141d857ece5645210584

Observation e850cd37-500d-46cf-8bd6-4f854682800d · inbound

SeaAlert: Robust Severity Classification and LLM-Based Information Extraction for Noisy Maritime Distress Communications cites this paper.

SeaAlert: Robust Severity Classification and LLM-Based Information Extraction for Noisy Maritime Distress Communications Towards Improving Adversarial Training of NLP Models

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-15T00:28:23.412616Z

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.

source=pdf_text observed=2026-05-15T00:26:09.423198Z digest=sha256:142a1400715ab440060a5c0d2b2264873bf1c1a562600f39688aff0baf3d3ca3

Observation e4442878-02ba-4e5e-b645-ee873f73d6cd · inbound

Evaluation of Adversarial Robustness in Arabic Language Models cites this paper.

Evaluation of Adversarial Robustness in Arabic Language Models Towards Improving Adversarial Training of NLP Models

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-01T01:26:18.169995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T01:26:18.169995Z digest=sha256:079e9b9755ce1a4da46181e718af3a1eb7989229985f8194bb61e11e5593086e