Paper Citation Record · LEDGER

Submix: Practical Private Prediction for Large-Scale Language Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2201.00971.

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

pith.paper-citation-record.v1
2201.00971 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T20:51:49.968645Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T00:27:29.602897Z

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 4ec8bfee-5971-43c3-af12-1cb77743afe8 · inbound

Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling cites this paper.

Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling Submix: Practical Private Prediction for Large-Scale Language Models

Reference 219

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T17:45:17.866905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f66ccbff-f1cc-46cb-8385-6dcdc5e175ed · inbound

AI Safety Landscape for Large Language Models: Taxonomy, State-of-the-art, and Future Directions cites this paper.

AI Safety Landscape for Large Language Models: Taxonomy, State-of-the-art, and Future Directions Submix: Practical Private Prediction for Large-Scale Language Models

Reference 246

Resolution: verified exact; arxiv_id, observed 2026-05-23T21:55:50.631140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 480166f5-8630-4e3c-997a-5ab58a03e5cd · inbound

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs cites this paper.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Submix: Practical Private Prediction for Large-Scale Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-09T20:51:49.968645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 393413fa-cb10-415c-ac5c-1e16e8ea19cf · inbound

InvisibleInk: High-Utility and Low-Cost Text Generation with Differential Privacy cites this paper.

InvisibleInk: High-Utility and Low-Cost Text Generation with Differential Privacy Submix: Practical Private Prediction for Large-Scale Language Models

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-19T06:52:07.901723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 58ede26c-6b8b-4e5d-9ccc-c03d5de3993c · inbound

Lower Bounds for Public-Private Learning under Distribution Shift cites this paper.

Lower Bounds for Public-Private Learning under Distribution Shift Submix: Practical Private Prediction for Large-Scale Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T14:56:03.948968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 162f9ecb-15e4-4d74-98f2-75d0702281ce · inbound

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

Public Data Assisted Differentially Private In-Context Learning Submix: Practical Private Prediction for Large-Scale Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T17:28:48.713228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 876c4554-c7ef-4d72-b30d-e52b25b328c8 · 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 Submix: Practical Private Prediction for Large-Scale Language Models

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-03T18:52:53.325810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5799a767-3db1-4858-82f7-181f46ab3138 · inbound

Chain-of-Authorization: Embedding authorization into large language models cites this paper.

Chain-of-Authorization: Embedding authorization into large language models Submix: Practical Private Prediction for Large-Scale Language Models

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-15T01:13:25.961832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 796a4726-a0da-4147-9411-5de715c53b90 · inbound

Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models cites this paper.

Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models Submix: Practical Private Prediction for Large-Scale Language Models

Reference 238

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T00:27:29.604289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d7bdf4cd-b487-499f-9d39-78bb6415fd39 · inbound

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

Optimal Domain-Aware Privacy Mechanisms for Synthetic Data Generation Submix: Practical Private Prediction for Large-Scale Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T16:26:23.711911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.