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Paper Citation Record · LEDGER

Synthetic Data Generation in Low-Resource Settings via Fine-Tuning of Large Language Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2310.01119.

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

pith.paper-citation-record.v1
2310.01119 v2

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 2026-08-08T04:52:23.234289Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-08T04:52:23.579485Z

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 5f0b294b-d50a-4257-9494-10810f97f766 · inbound

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data cites this paper.

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data Synthetic Data Generation in Low-Resource Settings via Fine-Tuning of Large Language Models

Reference 16

Resolution
verified exact
local_arxiv, observed 2026-08-08T04:52:23.584668Z

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=2026-08-08T04:52:23.234289Z digest=sha256:63a1e112ed7618cc761c5a3d19f2e4735a7ccf819db004a0b61d3058eb5967a1

Observation 2e99216b-1f39-4a7f-b84b-2515cfd9b6f4 · inbound

A Comprehensive Dataset for Human vs. AI Generated Text Detection cites this paper.

A Comprehensive Dataset for Human vs. AI Generated Text Detection Synthetic Data Generation in Low-Resource Settings via Fine-Tuning of Large Language Models

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-04T08:03:28.085266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T08:03:28.085266Z digest=sha256:75507b1d9b701d5744fcc5e66e9247e14603640220d2c8a0abddde6ec2ffb776