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Paper Citation Record · LEDGER

Adversarial Training for Large Neural Language Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2004.08994.

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

pith.paper-citation-record.v1
2004.08994 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 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 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:47:32.891878Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T13:45:45.877574Z

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 d0298edd-1500-48a1-8f1e-12ffcb9f0ca2 · inbound

Language Models are Few-Shot Learners cites this paper.

Language Models are Few-Shot Learners Adversarial Training for Large Neural Language Models

Reference 34

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T12:05:38.269836Z

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=2026-05-10T12:05:38.045330Z digest=sha256:97a40ad719ac0bba8b4fd0319c1c704b36363958c8bad578378d05a79c1dac02

Observation c14f9ae4-4ca9-4814-9f67-8f5951ab12f1 · inbound

DeBERTa: Decoding-enhanced BERT with Disentangled Attention cites this paper.

DeBERTa: Decoding-enhanced BERT with Disentangled Attention Adversarial Training for Large Neural Language Models

Reference 18

Resolution
metadata mismatch
arxiv_id, observed 2026-05-13T04:50:53.648799Z

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=2026-05-13T04:50:53.587891Z digest=sha256:e90fe6ab73b8289131bad5b76b15c36e733c7af567bdbc1bd685d9ebf4e7e63e

Observation 6605eece-ddc8-430e-81b4-b77f1597e8df · inbound

LaMDA: Language Models for Dialog Applications cites this paper.

LaMDA: Language Models for Dialog Applications Adversarial Training for Large Neural Language Models

Reference 102

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T03:17:32.433131Z

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=2026-05-12T03:17:32.272353Z digest=sha256:c346cfec4a5cbace9b7430ca7136d63dae4ec4db10011781796c31a98d98f863

Observation 92cc1e77-cf07-4fe0-8839-b54fec1c5f27 · inbound

Prompt Injection attack against LLM-integrated Applications cites this paper.

Prompt Injection attack against LLM-integrated Applications Adversarial Training for Large Neural Language Models

Reference 32

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T21:16:57.289769Z

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=2026-05-11T21:16:57.222178Z digest=sha256:004729973e098a2616eb5fe15ae5b119882cbfd99efa8d9e139099b2159e7ae2

Observation d2625364-ce3a-46ab-b9bd-fc91804c9f8c · inbound

GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts cites this paper.

GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts Adversarial Training for Large Neural Language Models

Reference 34

Resolution
metadata mismatch
arxiv_id, observed 2026-05-15T06:25:21.080606Z

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=2026-05-15T06:25:20.966510Z digest=sha256:e7dece909e2e6753918b89b97278a09ff0229663fe6e042318ac6dc996b32d68

Observation b6f71a6b-e6e9-45ce-8316-39a6f76bea67 · inbound

SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks cites this paper.

SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks Adversarial Training for Large Neural Language Models

Reference 32

Resolution
metadata mismatch
arxiv_id, observed 2026-05-14T17:11:00.782088Z

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=2026-05-14T17:11:00.639293Z digest=sha256:c699880811bd0fd2178ce79af72813675e2c44ab90fd7257883667c8f509f53e

Observation 0ab7be92-ba2b-4108-b48c-6759b07f22ab · inbound

Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models cites this paper.

Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models Adversarial Training for Large Neural Language Models

Reference 210

Resolution
verified exact
arxiv_id, observed 2026-05-18T06:38:36.968787Z

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=arxiv_source observed=2026-05-18T06:38:36.517935Z digest=sha256:f8edc2b7f0b7d8a144c761fa1b84808fbd8e892f3635427f1b12a308632e1511

Observation 9b6c3a27-f39b-4c65-b43d-116ab92bb805 · inbound

LLMs are Frequency Pattern Learners in Natural Language Inference cites this paper.

LLMs are Frequency Pattern Learners in Natural Language Inference Adversarial Training for Large Neural Language Models

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-07T13:47:32.891878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:47:32.891878Z digest=sha256:0d299ec4831959b67b4b579a0de87b93b0dd3e671b6ade029c50675dfd2ea4ae

Observation 8b547ac2-a353-4344-a0a0-525fd1af06e9 · inbound

PRM-Free Security Alignment of Large Models via Red Teaming and Adversarial Training cites this paper.

PRM-Free Security Alignment of Large Models via Red Teaming and Adversarial Training Adversarial Training for Large Neural Language Models

Reference 92

Resolution
unresolved
no resolver link, observed 2026-08-06T17:35:48.602842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:35:48.602842Z digest=sha256:65e6183b5e8eeefc8184fb0807cb15b084c628688f625ce3f0f47e9a208556fa

Observation b2b295e4-af94-4051-bdbb-ee969cb47e4f · inbound

Robust Biomedical Publication Type and Study Design Classification with Knowledge-Guided Perturbations cites this paper.

Robust Biomedical Publication Type and Study Design Classification with Knowledge-Guided Perturbations Adversarial Training for Large Neural Language Models

Reference 21

Resolution
metadata mismatch
arxiv_id, observed 2026-05-13T02:27:07.563124Z

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=2026-05-13T02:22:32.352808Z digest=sha256:ebbc4239061f1fa37ac54b6620aa6412df14402409656fd3ca80ec8701a1b509

Observation 57e73f9a-8a76-4e6d-a753-704c501a32f7 · inbound

Robust Biomedical Publication Type and Study Design Classification with Knowledge-Guided Perturbations cites this paper.

Robust Biomedical Publication Type and Study Design Classification with Knowledge-Guided Perturbations Adversarial Training for Large Neural Language Models

Reference 21

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T13:45:45.879179Z

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=2026-06-30T22:48:53.926480Z digest=sha256:fae523f221ac09af7ab79e924131246c7fcd6770a4b98e046e7a1121e79bde42