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Paper Citation Record · LEDGER

Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large 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:2406.12397.

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

pith.paper-citation-record.v1
2406.12397 v1

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 2026-08-07T06:34:17.273281+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:12:18.888836Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T07:32:59.970166Z

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 770527de-c0eb-433c-b12f-25502fc8fe94 · inbound

PuckTrick: A Library for Making Synthetic Data More Realistic cites this paper.

PuckTrick: A Library for Making Synthetic Data More Realistic Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-19T07:32:59.971471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-19T07:32:09.985556Z digest=sha256:6662afccd8b5fde4c2418a5a57b500c12e06653786fcdf6c512b84ef95dcdde4

Observation 5567f7ba-0961-48e8-9080-1a02a7cf09ed · inbound

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning cites this paper.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-06T23:12:18.888836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:12:18.888836Z digest=sha256:59806a6b6c68354641bb1623281a080843242374659ff8ea7c367cf0e41d3459

Observation ac0ac4bd-f31d-4580-9285-1517dea39f23 · inbound

Unlocking Speech Instruction Data Potential with Query Rewriting cites this paper.

Unlocking Speech Instruction Data Potential with Query Rewriting Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-06T18:21:31.668305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:21:31.668305Z digest=sha256:b938d20667aa7b209f0fed4de91685bf7737d5fbe53916bdb02fbe45e6ed1f47

Observation bd8b90c1-9805-43a8-a378-37f80ad64481 · inbound

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes cites this paper.

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-04T18:28:36.782722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T18:28:36.782722Z digest=sha256:82d6000a1e58db131289ac669d4ae8683dfc22f8882ffc7d8145c24cedf88e49

Observation 834f19cd-687b-4adf-b18b-d6449c077856 · inbound

Adversarial Arena: Crowdsourcing Data Generation through Interactive Competition cites this paper.

Adversarial Arena: Crowdsourcing Data Generation through Interactive Competition Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models

Reference 64

Resolution
verified exact
arxiv_id, observed 2026-05-10T11:10:09.449769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-10T04:55:43.987116Z digest=sha256:3f28e6c14260b88ebbf3e6d866aa00113bc58a716f585d03fa3929e0f1167665