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Paper Citation Record · LEDGER

Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2311.11202.

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

pith.paper-citation-record.v1
2311.11202 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 2026-08-08T06:32:00.761636+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:10:13.252061Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T09:07:48.089752Z

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 8045ba8a-88a9-493e-b61e-1024ca06bdcd · inbound

Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyond cites this paper.

Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyond Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-05-23T21:48:28.748500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-23T21:47:28.193374Z digest=sha256:75bf9a573c61fa8ee63600473726fd02466852f6c78b68a324878de45a76ac3e

Observation 72d5a0e0-35b0-49dd-8837-17c29dd6b31c · inbound

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification cites this paper.

When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-07T15:10:13.252061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:10:13.252061Z digest=sha256:f2fc4d35d5b8edbdfadf824624eaf5996c9bd2e3199f266f12c49244e8baf1da

Observation ab0996bd-84c5-404f-8d21-c12584dfa295 · inbound

Evian: Towards Explainable Visual Instruction-tuning Data Auditing cites this paper.

Evian: Towards Explainable Visual Instruction-tuning Data Auditing Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models

Reference 9

Resolution
malformed identifier
arxiv_id, observed 2026-05-10T00:54:48.557642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T00:51:40.069488Z digest=sha256:291ca0b98d9c217c5da4e2e2dd3e25673577593712f4679df1824e2a40674196

Observation 98edbffc-663e-492e-88bf-3aca594042cf · inbound

Noise-Aware Framework for Correcting Corrupted Labels cites this paper.

Noise-Aware Framework for Correcting Corrupted Labels Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models

Reference 22

Resolution
verified exact
arxiv_id, observed 2026-07-03T09:07:47.575473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-27T10:33:56.216151Z digest=sha256:3939458aaa2847a3974314d5d548b41d605d5cfdfbaae69ee700312488275ffe

Observation c925e469-c516-4e9f-ab56-5b7d9e428dbd · inbound

A Data-Centric Framework for Detecting and Correcting Corrupted Labels cites this paper.

A Data-Centric Framework for Detecting and Correcting Corrupted Labels Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models

Reference 27

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T09:07:48.091265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-27T10:31:12.491474Z digest=sha256:c00d6fd9c59911713f14dd5b8ac4792091bec2cd7dfae97104287b9a54b6ab42