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Paper Citation Record · LEDGER

Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2104.08678.

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

pith.paper-citation-record.v1
2104.08678 v3

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 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T23:35:38.514355Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T09:05:35.915889Z

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 556cc2a8-13c3-4457-8f0c-e32148bc4baf · inbound

Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned cites this paper.

Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-12T01:38:08.438644Z

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=2026-05-12T01:38:08.362920Z digest=sha256:daf90f9924782b33a63279bd82f803470fc6d8e0769c9dc6617d29ff419e93ca

Observation c02b18a0-084d-454c-94c9-51977fb37fab · inbound

ART: Automatic multi-step reasoning and tool-use for large language models cites this paper.

ART: Automatic multi-step reasoning and tool-use for large language models Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation

Reference 81

Resolution
metadata mismatch
arxiv_id, observed 2026-05-16T19:03:06.072736Z

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=2026-05-16T19:03:05.597295Z digest=sha256:7e163efd2d368a069d3fb915ef76666d8143891a3a1fa56d467538e4b6f9bde0

Observation 86796b41-e78d-4801-827d-050558a0f9cd · inbound

Jailbreaking Black Box Large Language Models in Twenty Queries cites this paper.

Jailbreaking Black Box Large Language Models in Twenty Queries Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation

Reference 25

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T09:48:33.275734Z

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=2026-05-12T09:48:31.721745Z digest=sha256:55cf2811aebb7399787ed0485896b884697e6277e4b338cb70642c27fb07198b

Observation 4d171c48-4e6e-483b-9792-b3715f84e2fa · inbound

MirrorCheck: Efficient Adversarial Defense for Vision-Language Models cites this paper.

MirrorCheck: Efficient Adversarial Defense for Vision-Language Models Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-25T09:05:35.918882Z

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=2026-05-25T09:03:31.136506Z digest=sha256:5f3d7f2c346a668d24fd12f67f70efb9b2a3abfbb694be543c94b9bbf53afebe

Observation 746e3596-308d-4da8-b88f-f260e5b181b7 · inbound

Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models cites this paper.

Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation

Reference 96

Resolution
verified exact
arxiv_id, observed 2026-05-17T14:43:30.130930Z

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=2026-05-17T14:43:29.496457Z digest=sha256:4bd2485c72f9b4a7c4525419b4435a9f3779d953d74163b0733207750e31ee74

Observation f9955273-cd81-41d7-bc03-fabd187a0312 · inbound

LLMs to Support a Domain Specific Knowledge Assistant cites this paper.

LLMs to Support a Domain Specific Knowledge Assistant Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-08T23:35:38.514355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T23:35:38.514355Z digest=sha256:6613c242dc39b04ab0113afd88e0b0accc666d9edc9f38ec7e986d06f470c9ae