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Paper Citation Record · LEDGER

Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2306.04140.

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

pith.paper-citation-record.v1
2306.04140 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 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 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T17:09:15.245871Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T07:55:33.032731Z

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 3c68e026-e093-43b6-b7b6-70898f9a3435 · inbound

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution cites this paper.

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions

Reference 18

Resolution
metadata mismatch
arxiv_id, observed 2026-05-16T08:12:35.396188Z

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.

source=arxiv_source observed=2026-05-16T08:12:30.984870Z digest=sha256:8d033406eebfb1a9c477ba5957f4c17c1fb21321f091d68860110fa9d88dbd1e

Observation 9033d16f-0fe9-4637-94c7-25a5e003a62f · inbound

BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation cites this paper.

BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-09T17:09:15.245871Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T17:09:15.245871Z digest=sha256:8272c1182261dd6b42f8897bb707148363e66e11170408d80f7a5cf5d71cceaa

Observation 4e65a109-73cf-4999-81e5-2b4fc6becbf0 · inbound

Measuring Diversity in Synthetic Datasets cites this paper.

Measuring Diversity in Synthetic Datasets Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-08T04:54:50.593096Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T04:54:50.593096Z digest=sha256:ad812baab84d5980310222f6efe8aba0df854d869bc151cf6f92e7c30d701e8f

Observation feb87ca6-dca0-48e0-b56d-b7139f33f997 · inbound

BiasFilter: An Inference-Time Debiasing Framework for Large Language Models cites this paper.

BiasFilter: An Inference-Time Debiasing Framework for Large Language Models Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-07T13:21:26.331281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:21:26.331281Z digest=sha256:806bb7d826c34519c677628195d0ade1b946fa97c9b33ffa28c55caf78fe6ec6

Observation aec73a19-4105-4a82-b245-13fd63fff72f · inbound

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead cites this paper.

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-06T21:36:25.441570Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:36:25.441570Z digest=sha256:0452140196f7dd54f6265e40b76f11ce7c50d230bd79a9c82e018bf46f37e817

Observation c822645e-8250-42cf-a761-11af2d83b692 · inbound

False Alarms, Real Damage: Adversarial Attacks Using LLM-based Models on Text-based Cyber Threat Intelligence Systems cites this paper.

False Alarms, Real Damage: Adversarial Attacks Using LLM-based Models on Text-based Cyber Threat Intelligence Systems Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions

Reference 47

Resolution
verified exact
arxiv_id, observed 2026-05-25T07:55:33.036941Z

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.

source=pdf_text observed=2026-05-25T07:54:51.925530Z digest=sha256:2560ad95cc2ac6f73b94eec16c66583ba0266634ada1c7c79d965cdd12d88f8c

Observation f4a26e47-dba7-459a-861f-93ecec154ea7 · inbound

StaAgent: An Agentic Framework for Testing Static Analyzers cites this paper.

StaAgent: An Agentic Framework for Testing Static Analyzers Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-06T15:50:37.839923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:50:37.839923Z digest=sha256:6d2c27e6ee93d07884173c6834fdb5991cd16a8f7eb3e40ddbaaec1b44cdfef9

Observation 7c66b009-e0b2-4983-861d-54b745bed5ca · inbound

Leak@$k$: Unlearning Does Not Make LLMs Forget Under Probabilistic Decoding cites this paper.

Leak@$k$: Unlearning Does Not Make LLMs Forget Under Probabilistic Decoding Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-03T23:39:04.594674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T23:39:04.594674Z digest=sha256:b0b1a033e8294fedee2e83390c9b0814646250556c958553a1b9f2d0755f849f

Observation d5ee0dfc-3343-43fa-b773-c0bbc967ad42 · inbound

Synthetic Interaction Data for Scalable Personalization in Large Language Models cites this paper.

Synthetic Interaction Data for Scalable Personalization in Large Language Models Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-02T23:53:01.419240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T23:53:01.419240Z digest=sha256:b849b68ab84deb84ade637bb81fcbd708a061a2be1c6c28cd95c306f53e022ac