Paper Citation Record · LEDGER

EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Models

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2404.12404.

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

pith.paper-citation-record.v1
2404.12404 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:55:18.163364Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T10:07:56.187134Z

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 9d72b498-5228-43df-9534-35b5349e7ad5 · inbound

Improving Equity in Health Modeling with GPT4-Turbo Generated Synthetic Data: A Comparative Study cites this paper.

Improving Equity in Health Modeling with GPT4-Turbo Generated Synthetic Data: A Comparative Study EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T10:44:17.741478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8dff20a9-18a4-4d03-b9c9-646dd2faf861 · inbound

Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data cites this paper.

Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Models

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-16T11:55:18.163364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12266270-5f0f-4e53-a4c3-90a9ac57da3a · inbound

A Note on Statistically Accurate Tabular Data Generation Using Large Language Models cites this paper.

A Note on Statistically Accurate Tabular Data Generation Using Large Language Models EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T00:49:59.166406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7b17d21-f77c-4a21-aed7-966dadeedde9 · inbound

Risk In Context: Benchmarking Privacy Leakage of Foundation Models in Synthetic Tabular Data Generation cites this paper.

Risk In Context: Benchmarking Privacy Leakage of Foundation Models in Synthetic Tabular Data Generation EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T15:04:00.802984Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24c64600-ae49-4392-812c-0d7c6e7c0cb0 · inbound

A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data cites this paper.

A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Models

Reference 101

Resolution: unresolved; no resolver link, observed 2026-08-03T08:15:21.447169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 920334d1-0453-47a5-9336-6c849da95d25 · inbound

Categorical Prior Lock-in: Why In-Context Learning Fails for Structured Data cites this paper.

Categorical Prior Lock-in: Why In-Context Learning Fails for Structured Data EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Models

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-03T10:07:56.188299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.