Paper Citation Record · LEDGER

Harnessing large-language models to generate private synthetic text

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2306.01684.

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

pith.paper-citation-record.v1
2306.01684 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 2026-08-08T06:32:00.761636+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T21:50:03.083411Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

2
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 7c03d306-930e-407f-9ae7-c4e687f59c51 · inbound

ShieldGemma: Generative AI Content Moderation Based on Gemma cites this paper.

ShieldGemma: Generative AI Content Moderation Based on Gemma Harnessing large-language models to generate private synthetic text

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:17:39.515674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e5403b03-8505-4786-b3b0-77c66e1024c9 · inbound

Language Agents as Digital Representatives in Collective Decision-Making cites this paper.

Language Agents as Digital Representatives in Collective Decision-Making Harnessing large-language models to generate private synthetic text

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T21:50:03.083411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66ce17f6-322d-475e-a918-552dcd5d40a0 · inbound

Synthesizing and Adapting Error Correction Data for Mobile Large Language Model Applications cites this paper.

Synthesizing and Adapting Error Correction Data for Mobile Large Language Model Applications Harnessing large-language models to generate private synthetic text

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T14:34:03.541612Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0247f1d-b273-47c3-a4b0-edf0355478e9 · inbound

Clustering and Median Aggregation Improve Differentially Private Inference cites this paper.

Clustering and Median Aggregation Improve Differentially Private Inference Harnessing large-language models to generate private synthetic text

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T10:47:28.344559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8bc14da6-e758-46d7-a4f7-a5883708b2d2 · 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 Harnessing large-language models to generate private synthetic text

Reference 170

Resolution: unresolved; no resolver link, observed 2026-08-06T21:36:35.982399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2b551c5-8ada-4b67-be30-ee7243b40bb7 · inbound

How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy cites this paper.

How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy Harnessing large-language models to generate private synthetic text

Reference 133

Resolution: unresolved; no resolver link, observed 2026-08-03T18:52:57.840718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec03c3c5-8d35-4e19-b336-ff6452eda164 · inbound

MAPLE: Metadata Augmented Private Language Evolution cites this paper.

MAPLE: Metadata Augmented Private Language Evolution Harnessing large-language models to generate private synthetic text

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-04T05:59:27.385496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df3c6153-26d4-42ef-ae6e-1d71417b5060 · inbound

DP-OPD: Differentially Private On-Policy Distillation for Language Models cites this paper.

DP-OPD: Differentially Private On-Policy Distillation for Language Models Harnessing large-language models to generate private synthetic text

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T23:15:48.459104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 99168218-17d3-453b-9e4f-f022008a755a · inbound

FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion cites this paper.

FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion Harnessing large-language models to generate private synthetic text

Reference 121

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T12:56:05.828261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3f53ba47-d4db-4138-a893-fe8ebcb2bf36 · inbound

DP-SelFT: Differentially Private Selective Fine-Tuning for Large Language Models cites this paper.

DP-SelFT: Differentially Private Selective Fine-Tuning for Large Language Models Harnessing large-language models to generate private synthetic text

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:38:19.403162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d9b100ec-674d-4b5e-a47e-be41e2eaf72f · inbound

ContinuousBench: Can Differentially Private Synthetic Text Improve Capabilities? cites this paper.

ContinuousBench: Can Differentially Private Synthetic Text Improve Capabilities? Harnessing large-language models to generate private synthetic text

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-06-28T15:42:21.721786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 45279419-782e-4f38-affa-1fbe26a26c7b · inbound

Asymptotic Optimality of the High-Dimensional Gaussian Mechanism and Improved Low-Dimensional Mechanisms for Differential Privacy cites this paper.

Asymptotic Optimality of the High-Dimensional Gaussian Mechanism and Improved Low-Dimensional Mechanisms for Differential Privacy Harnessing large-language models to generate private synthetic text

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T23:47:27.622065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 88157295-aba4-449a-829f-e8b4d9a7e390 · inbound

Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data cites this paper.

Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data Harnessing large-language models to generate private synthetic text

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-02T11:15:50.068311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49d52a7d-a37f-4e2d-bdef-e1d0aff51826 · inbound

Optimal Domain-Aware Privacy Mechanisms for Synthetic Data Generation cites this paper.

Optimal Domain-Aware Privacy Mechanisms for Synthetic Data Generation Harnessing large-language models to generate private synthetic text

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T16:26:23.697959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.