Paper Citation Record · LEDGER

LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2402.14568.

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

pith.paper-citation-record.v1
2402.14568 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T23:31:15.975108Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T11:01:31.970908Z

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 a6c7de0d-00d6-4b2a-9b35-51227c7635a4 · inbound

GerPS-Compare: Comparing NER methods for legal norm analysis cites this paper.

GerPS-Compare: Comparing NER methods for legal norm analysis LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T23:31:15.975108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 354656a6-11aa-43a4-b518-3b8f77704996 · inbound

Mind the Gap: Towards Generalizable Autonomous Penetration Testing via Domain Randomization and Meta-Reinforcement Learning cites this paper.

Mind the Gap: Towards Generalizable Autonomous Penetration Testing via Domain Randomization and Meta-Reinforcement Learning LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-11T21:51:38.416155Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8cdf34cd-faad-46d7-82e3-a260734a8446 · inbound

A Benchmark and Robustness Study of In-Context-Learning with Large Language Models in Music Entity Detection cites this paper.

A Benchmark and Robustness Study of In-Context-Learning with Large Language Models in Music Entity Detection LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-11T14:35:37.648881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b898bb7-b9a8-4eed-8d51-f42592b3ee7d · inbound

Multimodal Large Language Models for Image, Text, and Speech Data Augmentation: A Survey cites this paper.

Multimodal Large Language Models for Image, Text, and Speech Data Augmentation: A Survey LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition

Reference 193

Resolution: unresolved; no resolver link, observed 2026-08-10T04:36:37.895427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efec5ead-f218-4795-a65b-1ac37241f614 · inbound

Text Data Augmentation for Large Language Models: A Comprehensive Survey of Methods, Challenges, and Opportunities cites this paper.

Text Data Augmentation for Large Language Models: A Comprehensive Survey of Methods, Challenges, and Opportunities LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-09T22:17:46.798537Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b8dba37-0569-4a98-a88e-6db513a20450 · inbound

Measuring Diversity in Synthetic Datasets cites this paper.

Measuring Diversity in Synthetic Datasets LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-08T04:54:50.949266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 110aa6e9-78fb-4ec5-8c1f-5228c68bf326 · inbound

Prompt Candidates, then Distill: A Teacher-Student Framework for LLM-driven Data Annotation cites this paper.

Prompt Candidates, then Distill: A Teacher-Student Framework for LLM-driven Data Annotation LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition

Reference 9

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T11:01:32.025563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5922a9a9-8648-4cf7-b116-e7b7d874fc53 · inbound

Imbalanced Data Clustering via Targeted Data Augmentation Using GMM and LLM cites this paper.

Imbalanced Data Clustering via Targeted Data Augmentation Using GMM and LLM LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-03T00:55:00.014770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.