Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2311.11202.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T23:22:02.944856Z
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 2026-08-14T06:32:32.682623+00:00.
Observation bcebd9d2-39c8-4a55-bf80-4dd1abf7e8a2 · inbound
Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes cites this paper.
Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models
Reference 51
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:22:02.944856Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation be8a3672-dea8-4e68-9652-40db301976d0 · inbound
Noise-Resilient Point-wise Anomaly Detection in Time Series Using Weak Segment Labels cites this paper.
Noise-Resilient Point-wise Anomaly Detection in Time Series Using Weak Segment Labels Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models
Reference 69
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T17:45:46.115750Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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.
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 2026-08-14T06:32:32.682623+00:00.
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 2026-08-14T06:32:32.682623+00:00.
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 2026-08-14T06:32:32.682623+00:00.