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Paper Citation Record · LEDGER

Empowering Large Language Models for Textual Data Augmentation

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2404.17642.

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

pith.paper-citation-record.v1
2404.17642 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T04:54:50.754071Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T15:36:40.460310Z

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 29c9d34d-3fc1-4e14-bd16-94ef907260cd · inbound

Measuring Diversity in Synthetic Datasets cites this paper.

Measuring Diversity in Synthetic Datasets Empowering Large Language Models for Textual Data Augmentation

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-08T04:54:50.754071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T04:54:50.754071Z digest=sha256:4e1fb798d4e4cebe79e1c2eab4cab12d648d44395ad83eeca7fe085178d60a6d

Observation a83aa9d7-1454-4345-8998-bcb58d832bd6 · inbound

SynthCTI: LLM-Driven Synthetic CTI Generation to enhance MITRE Technique Mapping cites this paper.

SynthCTI: LLM-Driven Synthetic CTI Generation to enhance MITRE Technique Mapping Empowering Large Language Models for Textual Data Augmentation

Reference 11

Resolution
verified exact
local_arxiv, observed 2026-08-06T15:36:40.513135Z

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=2026-08-06T15:36:36.267656Z digest=sha256:5275188f2172e9f34756614e314ccb4a90d6be918802e457a634aba11bc74dae