Paper Citation Record · LEDGER

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs

As of 10 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2501.19287.

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

pith.paper-citation-record.v1
2501.19287 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T20:51:50.089984Z

measured 50 of 50 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

50 of 50 outbound references displayed

verified exact2
verified fuzzy10
unresolved37
parse uncertain0
malformed identifier0
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External citation measurements

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Outbound references

Observation 808528f3-9742-4e3e-9e1d-a614c9e90715 · outbound

This paper cites write newline.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs write newline

Reference 1

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Observation bc9ef88a-7c74-4501-82a9-06a428179f26 · outbound

This paper cites Privacy amplification by subsampling: Tight analyses via couplings and divergences.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Privacy amplification by subsampling: Tight analyses via couplings and divergences

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:51:50.834670Z

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Observation 76ab7b60-36cb-4f4f-afaa-820a1a89aae4 · outbound

This paper cites Hypothesis testing interpretations and renyi differential privacy.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Hypothesis testing interpretations and renyi differential privacy

Reference 3

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Observation e791d802-0d57-4ef4-a46f-ccf087d57c15 · outbound

This paper cites D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

Reference 4

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Observation 62b9262a-9a08-4344-ae83-fb910a0907e2 · outbound

This paper cites Stealing Part of a Production Language Model.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Stealing Part of a Production Language Model

Reference 5

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Observation bc7d88ac-012a-433f-b4ef-ff114003e696 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T20:51:49.936033Z

Source-reported events for the cited work

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Observation d33a6ebd-0e12-49a5-85b8-997294928950 · outbound

This paper cites On the privacy risk of in-context learning.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs On the privacy risk of in-context learning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:51:50.810504Z

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

Observation 33d8f48b-f27a-4e42-84f9-5dfd88f9a215 · outbound

This paper cites Flocks of stochastic parrots: Differentially private prompt learning for large language models.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Flocks of stochastic parrots: Differentially private prompt learning for large language models

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:51:50.799448Z

Source-reported events for the cited work

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Observation 737cc81d-01ec-4883-a0d5-c41a2b90cbed · outbound

This paper cites The Llama 3 Herd of Models.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs The Llama 3 Herd of Models

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 136d5d45-bf8f-4f7a-8f60-2245602b7c54 · outbound

This paper cites Differential privacy.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Differential privacy

Reference 10

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Observation 0189da05-4afb-4b2b-a665-8395d2cfab76 · outbound

This paper cites and Feldman, V.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs and Feldman, V

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:51:50.782338Z

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 36e0bbfe-e5f2-4f9b-ae5f-f2adb2a4f26c · outbound

This paper cites The algorithmic foundations of differential privacy.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs The algorithmic foundations of differential privacy

Reference 12

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Observation 79a0372e-e2cb-40fd-8bc5-07c8380447b4 · outbound

This paper cites Hierarchical Neural Story Generation.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Hierarchical Neural Story Generation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-09T20:51:49.961841Z

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Observation da358724-4a08-4e93-a6b3-b2832b627140 · outbound

This paper cites Differentially Private Next-Token Prediction of Large Language Models.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Differentially Private Next-Token Prediction of Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T20:51:49.965171Z

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

This paper cites Submix: Practical Private Prediction for Large-Scale Language Models.

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

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Observation e9998b91-fde9-4101-afed-4de0aa3a44c7 · outbound

This paper cites SAMS um corpus: A human-annotated dialogue dataset for abstractive summarization.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs SAMS um corpus: A human-annotated dialogue dataset for abstractive summarization

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-09T20:51:49.972152Z

Source-reported events for the cited work

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Observation 5ad38351-9cd6-455d-be7c-ee506c375c0a · outbound

This paper cites Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternatives.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternatives

Reference 17

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Source-reported events for the cited work

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Observation b1c5c8ca-a98a-43f5-89ea-25bff54e137f · outbound

This paper cites DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T20:51:49.979086Z

Source-reported events for the cited work

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Observation 4115e0bd-59b8-4448-9aa8-d023106aefb2 · outbound

This paper cites Local differential privacy for sampling.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Local differential privacy for sampling

Reference 19

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Source-reported events for the cited work

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Observation af8f52f4-788c-41f8-920e-314b07ce59ed · outbound

This paper cites AnglE-optimized Text Embeddings.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs AnglE-optimized Text Embeddings

Reference 20

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Observation 6b79170b-93bf-45f6-8e81-cab6a1ed7dc4 · outbound

This paper cites Rouge: A package for automatic evaluation of summaries.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Rouge: A package for automatic evaluation of summaries

Reference 21

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Observation 26260a7d-58ec-4e53-adb7-850b15d06720 · outbound

This paper cites Differentially Private Decoding in Large Language Models.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Differentially Private Decoding in Large Language Models

Reference 22

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Observation c6871c72-d3f7-45fb-b654-b21749b4067a · outbound

This paper cites Noisy Channel Language Model Prompting for Few-Shot Text Classification.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Noisy Channel Language Model Prompting for Few-Shot Text Classification

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T20:51:49.996466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b23baa7b-720e-4b2d-a48f-d37ff560db75 · outbound

This paper cites Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

Reference 24

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Observation 64896e64-6e73-4177-850b-12e680137dde · outbound

This paper cites R \'e nyi differential privacy.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs R \'e nyi differential privacy

Reference 25

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Observation a72d5904-312e-49af-b510-6a8bd14def6f · outbound

This paper cites Smooth sensitivity and sampling in private data analysis.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Smooth sensitivity and sampling in private data analysis

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-09T20:51:50.008023Z

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Observation 6ca606dd-87be-48b6-a173-ff94c85eb830 · outbound

This paper cites The E2E Dataset: New Challenges For End-to-End Generation.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs The E2E Dataset: New Challenges For End-to-End Generation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-09T20:51:50.012550Z

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Observation 4c19ae90-d1c4-4a80-b5e6-3264d10035a3 · outbound

This paper cites Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data

Reference 28

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Observation d14c54b9-16a9-4816-9d11-e4a2dc7c8355 · outbound

This paper cites Scalable Private Learning with PATE.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Scalable Private Learning with PATE

Reference 29

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Observation b5dda8ba-3e12-4343-89a3-34b4f488d937 · outbound

This paper cites Are Chatbots Ready for Privacy-Sensitive Applications? An Investigation into Input Regurgitation and Prompt-Induced Sanitization.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Are Chatbots Ready for Privacy-Sensitive Applications? An Investigation into Input Regurgitation and Prompt-Induced Sanitization

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-09T20:51:50.023455Z

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Observation 083c4571-95e8-4a03-af42-ca3fd5975631 · outbound

This paper cites Language models are unsupervised multitask learners.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Language models are unsupervised multitask learners

Reference 31

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Observation e749f19a-149e-48dd-8a2f-544adb963195 · outbound

This paper cites Membership inference attacks against machine learning models.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Membership inference attacks against machine learning models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-09T20:51:50.029811Z

Source-reported events for the cited work

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Observation bff61a7e-38b0-4317-9ebc-76edb1f7f1d6 · outbound

This paper cites Composition of Differential Privacy & Privacy Amplification by Subsampling.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Composition of Differential Privacy & Privacy Amplification by Subsampling

Reference 33

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Observation 6f7dbfb2-a126-4553-b61b-3f1b9b55ea43 · outbound

This paper cites Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-09T20:51:50.036302Z

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Observation b60801c0-2b8d-481a-b3e0-03e70a93dc29 · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Gemma 2: Improving Open Language Models at a Practical Size

Reference 35

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Observation 588a4c89-d828-4cfc-bdab-712e4d472d24 · outbound

This paper cites L., and He, H.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs L., and He, H

Reference 36

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Observation 1c1fb981-b1ca-42e9-9816-8aa0ef8ce5e7 · outbound

This paper cites and Harremos, P.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs and Harremos, P

Reference 37

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Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Unresolved cited work

Reference 38

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Observation 20dcebce-9dc2-40b3-baa4-8022853f09e7 · outbound

This paper cites Decodingtrust: A comprehensive assessment of trustworthiness in gpt models.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Decodingtrust: A comprehensive assessment of trustworthiness in gpt models

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:51:50.693920Z

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 9a1efcae-f6af-4b52-9383-f62f23e985d3 · outbound

This paper cites Privacy for free: Posterior sampling and stochastic gradient monte carlo.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Privacy for free: Posterior sampling and stochastic gradient monte carlo

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:51:50.683808Z

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 10c29113-b853-4bc6-9846-fe46aa9f2ba4 · outbound

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Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Unresolved cited work

Reference 41

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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 41ce6bf4-8be9-4249-b8ef-f2f69a95b4bb · outbound

This paper cites Larger language models do in-context learning differently.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Larger language models do in-context learning differently

Reference 42

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Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Privacy-Preserving In-Context Learning for Large Language Models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-09T20:51:50.066321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e061895-0a09-41a9-bdd5-101b9f5582d6 · outbound

This paper cites Differentially Private Synthetic Data via Foundation Model APIs 2: Text.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Differentially Private Synthetic Data via Foundation Model APIs 2: Text

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-09T20:51:50.069981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44ba2702-304b-41ee-9e7a-39a41d22ebda · outbound

This paper cites Context-aware decoding reduces hallucination in query-focused summarization.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Context-aware decoding reduces hallucination in query-focused summarization

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-08-09T20:51:50.394144Z

Source-reported events for the cited work

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Observation 1017b0a2-8886-49c5-8fb5-47385b984fff · outbound

This paper cites Privacy risk in machine learning: Analyzing the connection to overfitting.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Privacy risk in machine learning: Analyzing the connection to overfitting

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:51:50.663130Z

Source-reported events for the cited work

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Observation 64c31f16-3636-4dd7-86e1-38639dfbc252 · outbound

This paper cites BERTScore: Evaluating Text Generation with BERT.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs BERTScore: Evaluating Text Generation with BERT

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-09T20:51:50.079244Z

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This paper cites Sentence Simplification with Deep Reinforcement Learning.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Sentence Simplification with Deep Reinforcement Learning

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-08-09T20:51:50.136676Z

Source-reported events for the cited work

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Observation fd8e435b-ce0a-4183-8575-4ca8f188f370 · outbound

This paper cites Character-level convolutional networks for text classification.

Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Character-level convolutional networks for text classification

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-09T20:51:50.086716Z

Source-reported events for the cited work

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Observation 4a758ab8-32b2-4ca4-b1bf-851d0c5c4396 · outbound

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Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs Calibrate before use: Improving few-shot performance of language models

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:51:50.645821Z

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.

Pith citing papers

No inbound Pith citation observations are available.