Paper Citation Record · LEDGER

Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2310.07849.

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

pith.paper-citation-record.v1
2310.07849 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T17:37:07.085762Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T11:26:54.197769Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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malformed identifier0
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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 ce9176b9-314f-4206-a948-aff65bd870bd · inbound

Preserving Knowledge in Large Language Model with Model-Agnostic Self-Decompression cites this paper.

Preserving Knowledge in Large Language Model with Model-Agnostic Self-Decompression Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-24T00:13:39.483042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c3141f0c-ebb9-45f5-aff6-ab04f5dbdf30 · inbound

Measuring Diversity in Synthetic Datasets cites this paper.

Measuring Diversity in Synthetic Datasets Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-08T04:54:50.759083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38398225-b331-47e4-9176-0d770e9117c0 · inbound

Few-shot LLM Synthetic Data with Distribution Matching cites this paper.

Few-shot LLM Synthetic Data with Distribution Matching Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-08T17:20:36.513361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88629cc0-c338-4644-8e4e-4199363cc19e · inbound

Two-Stage Representation Learning for Analyzing Movement Behavior Dynamics in People Living with Dementia cites this paper.

Two-Stage Representation Learning for Analyzing Movement Behavior Dynamics in People Living with Dementia Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T22:27:05.612867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e9efebb-d5d6-41c9-9a2d-40ba3f890e1b · inbound

Analyzing Patient Daily Movement Behavior Dynamics Using Two-Stage Encoding Model cites this paper.

Analyzing Patient Daily Movement Behavior Dynamics Using Two-Stage Encoding Model Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T20:03:07.392558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be984c06-ff05-4fdc-b746-d9d8336e8d38 · inbound

The Application of MATEC (Multi-AI Agent Team Care) Framework in Sepsis Care cites this paper.

The Application of MATEC (Multi-AI Agent Team Care) Framework in Sepsis Care Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-08T17:37:07.085762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44908277-6858-4caf-9c36-d779ff6d6073 · inbound

TF1-EN-3M: Three Million Synthetic Moral Fables for Training Small, Open Language Models cites this paper.

TF1-EN-3M: Three Million Synthetic Moral Fables for Training Small, Open Language Models Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-22T19:01:57.827379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 39907f64-78a5-440c-9d4d-7b7d0612d9ea · inbound

Calibrating Pre-trained Language Classifiers on LLM-generated Noisy Labels via Iterative Refinement cites this paper.

Calibrating Pre-trained Language Classifiers on LLM-generated Noisy Labels via Iterative Refinement Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T14:14:22.012235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c6f4a5a-d269-4ee1-9bef-84d9ec122595 · inbound

Foundation Model Empowered Synesthesia of Machines (SoM): AI-native Intelligent Multi-Modal Sensing-Communication Integration cites this paper.

Foundation Model Empowered Synesthesia of Machines (SoM): AI-native Intelligent Multi-Modal Sensing-Communication Integration Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T05:33:52.644359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c87fad9-1675-4c54-a5c8-50224cb584aa · 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 Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:18.977169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e733372e-0a88-4937-8dad-141d0492ae00 · 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 Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T15:04:00.684227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2433f0c8-50a1-40b8-bd4e-6180fb200c5d · inbound

SeaAlert: Robust Severity Classification and LLM-Based Information Extraction for Noisy Maritime Distress Communications cites this paper.

SeaAlert: Robust Severity Classification and LLM-Based Information Extraction for Noisy Maritime Distress Communications Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-15T00:28:23.407221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 51f2238c-a2de-4b55-ace4-3f1e8757b35c · inbound

BEATS: Bootstrapping E-commerce Attribute Taxonomies for Search through Iterative Human-AI Collaboration cites this paper.

BEATS: Bootstrapping E-commerce Attribute Taxonomies for Search through Iterative Human-AI Collaboration Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-02T11:26:54.200039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f5c61a87-2e72-4e57-aa47-fd0fe5303dab · inbound

Optimizing the Preconditioner: A Black-box Online-to-Nonconvex Conversion with Static Regret Minimization Oracles cites this paper.

Optimizing the Preconditioner: A Black-box Online-to-Nonconvex Conversion with Static Regret Minimization Oracles Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations

Reference 200

Resolution: unresolved; no resolver link, observed 2026-08-01T17:38:08.904955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.