Paper Citation Record · LEDGER

Differentially Private Fine-tuning of Language Models

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 25 inbound Pith citation observations for arXiv:2110.06500.

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

pith.paper-citation-record.v1
2110.06500 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 25 of 25 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T19:11:57.435191Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T08:29:41.295298Z

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 ae74ea7c-2e2c-4e18-a075-b5b9164c3bf7 · inbound

The False Promise of Imitating Proprietary LLMs cites this paper.

The False Promise of Imitating Proprietary LLMs Differentially Private Fine-tuning of Language Models

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T06:54:31.322412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 260aa599-7632-4582-819e-15ea40a85301 · inbound

ConfusionPrompt: Practical Private Inference for Online Large Language Models cites this paper.

ConfusionPrompt: Practical Private Inference for Online Large Language Models Differentially Private Fine-tuning of Language Models

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-05-24T04:58:54.900344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a6debab8-3cc4-4b0b-8cff-800bd81b9a21 · inbound

Industry Practitioners Perspectives on AI Model Quality: Perceptions, Challenges, and Solutions cites this paper.

Industry Practitioners Perspectives on AI Model Quality: Perceptions, Challenges, and Solutions Differentially Private Fine-tuning of Language Models

Reference 139

Resolution: verified exact; arxiv_id, observed 2026-05-24T04:23:52.783217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b67333cf-64d1-480d-a070-782540281fb5 · inbound

Towards the Anonymization of the Language Modeling cites this paper.

Towards the Anonymization of the Language Modeling Differentially Private Fine-tuning of Language Models

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-23T06:32:39.496907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4709dde3-9eca-44c9-9b65-565f40fb7a01 · inbound

FedShield-LLM: A Secure and Scalable Federated Fine-Tuned Large Language Model cites this paper.

FedShield-LLM: A Secure and Scalable Federated Fine-Tuned Large Language Model Differentially Private Fine-tuning of Language Models

Reference 44

Resolution: metadata mismatch; arxiv_id, observed 2026-05-22T01:44:30.369790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a1f65a3b-00f5-4ef3-a864-6a15fd13490c · inbound

Memory-Efficient Differentially Private Training with Gradient Random Projection cites this paper.

Memory-Efficient Differentially Private Training with Gradient Random Projection Differentially Private Fine-tuning of Language Models

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:50:47.467243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 08b4b149-bc7a-4117-8013-6250178a2b9c · inbound

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection cites this paper.

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection Differentially Private Fine-tuning of Language Models

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-04T19:11:57.435191Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f3af31e-05f6-4ae7-925b-7619d522ae67 · inbound

Public Data Assisted Differentially Private In-Context Learning cites this paper.

Public Data Assisted Differentially Private In-Context Learning Differentially Private Fine-tuning of Language Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-04T17:28:50.996266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b43cc37a-fbfc-4eed-b71f-3e578f6c51bb · inbound

Differentially-private text generation degrades output language quality cites this paper.

Differentially-private text generation degrades output language quality Differentially Private Fine-tuning of Language Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-04T17:03:13.505526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 776659d6-0a5a-4f2e-bdd1-b40c3ac10bcc · inbound

Forget What's Sensitive, Remember What Matters: Token-Level Differential Privacy in Memory Sculpting for Continual Learning cites this paper.

Forget What's Sensitive, Remember What Matters: Token-Level Differential Privacy in Memory Sculpting for Continual Learning Differentially Private Fine-tuning of Language Models

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-25T08:30:31.812981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1c3bf0f5-7db8-43a6-b2b8-2d11f3281cf6 · inbound

Re-examining Low Rank adaptation for private LLM fine-tuning cites this paper.

Re-examining Low Rank adaptation for private LLM fine-tuning Differentially Private Fine-tuning of Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T13:23:19.701681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a7d745c-2c63-486e-a62b-9cbd6c2b0de5 · inbound

Membership Inference Attacks on Tokenizers of Large Language Models cites this paper.

Membership Inference Attacks on Tokenizers of Large Language Models Differentially Private Fine-tuning of Language Models

Reference 105

Resolution: unresolved; no resolver link, observed 2026-08-04T11:23:22.202539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c76114fd-300f-4417-9610-a96510aa5279 · 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 Differentially Private Fine-tuning of Language Models

Reference 266

Resolution: unresolved; no resolver link, observed 2026-08-03T18:53:11.031697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0fa656be-9b48-4765-966c-ffa8f65f7f49 · inbound

In-Context Probing for Membership Inference in Fine-Tuned Language Models cites this paper.

In-Context Probing for Membership Inference in Fine-Tuned Language Models Differentially Private Fine-tuning of Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T15:38:38.260190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98d02fb2-06a1-47b0-9714-1858adf65f8c · inbound

GroupGPT: A Token-efficient and Privacy-preserving Agentic Framework for Multi-User Chat Assistant cites this paper.

GroupGPT: A Token-efficient and Privacy-preserving Agentic Framework for Multi-User Chat Assistant Differentially Private Fine-tuning of Language Models

Reference 76

Resolution: verified exact; arxiv_id, observed 2026-05-15T18:30:14.378808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 08f79bd1-adc6-46fc-ade4-15e072c632fd · inbound

SLM Finetuning for Natural Language to Domain Specific Code Generation in Production cites this paper.

SLM Finetuning for Natural Language to Domain Specific Code Generation in Production Differentially Private Fine-tuning of Language Models

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:26:00.845144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9e6e2780-234f-4d5c-9e9d-bb9bc01be6d4 · inbound

Low-Rank Adaptation Redux for Large Models cites this paper.

Low-Rank Adaptation Redux for Large Models Differentially Private Fine-tuning of Language Models

Reference 226

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T14:26:03.838691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8b0fd4ef-e250-4005-9a13-9f4de961c532 · inbound

Beyond Individual Mimicry: Constructing Human-Like Social network with Graph-Augmented LLM Agents cites this paper.

Beyond Individual Mimicry: Constructing Human-Like Social network with Graph-Augmented LLM Agents Differentially Private Fine-tuning of Language Models

Reference 80

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T21:19:27.906926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c1cee4e5-94ef-4007-a719-5d7b05012106 · inbound

Probing Privacy Leaks in LLM-based Code Generation via Test Generation cites this paper.

Probing Privacy Leaks in LLM-based Code Generation via Test Generation Differentially Private Fine-tuning of Language Models

Reference 15

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T16:22:39.571884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f5f81c92-e052-4616-86dc-16637c38ede2 · 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 Differentially Private Fine-tuning of Language Models

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:38:19.381523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation eb26dbee-b79e-4981-8684-7638623e308c · inbound

Private and Stable Test-Time Adaptation with Differential Privacy cites this paper.

Private and Stable Test-Time Adaptation with Differential Privacy Differentially Private Fine-tuning of Language Models

Reference 34

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T22:36:17.163634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1e51e073-2328-4e70-84c7-4e8dcb51185d · inbound

SharedRequest: Privacy-Preserving Model-Agnostic Inference for Large Language Models cites this paper.

SharedRequest: Privacy-Preserving Model-Agnostic Inference for Large Language Models Differentially Private Fine-tuning of Language Models

Reference 50

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T09:26:51.564972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b7880f4a-a4da-4ce8-bfdc-5994d6040aa1 · inbound

Unveiling Privacy Risks in Multi-modal Large Language Models: Task-specific Vulnerabilities and Mitigation Challenges cites this paper.

Unveiling Privacy Risks in Multi-modal Large Language Models: Task-specific Vulnerabilities and Mitigation Challenges Differentially Private Fine-tuning of Language Models

Reference 44

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T01:27:30.932613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 68be48d9-ae2b-4745-9f12-24ab3e6f2b71 · inbound

PeerCheck: Enhancing LLM-Generated Academic Reviews Towards Human-Level Quality cites this paper.

PeerCheck: Enhancing LLM-Generated Academic Reviews Towards Human-Level Quality Differentially Private Fine-tuning of Language Models

Reference 86

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T04:09:34.577689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation cc13f7aa-5571-42fd-971d-4c8e1bcce5de · inbound

$\pi$-RAG: Oblivious Retrieval via Semantic Quantization and Transcendental Addressing for Large Language Models cites this paper.

$\pi$-RAG: Oblivious Retrieval via Semantic Quantization and Transcendental Addressing for Large Language Models Differentially Private Fine-tuning of Language Models

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T08:29:41.297061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.