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Paper Citation Record · LEDGER

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation

As of 19 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2504.18147.

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

pith.paper-citation-record.v1
2504.18147 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:29:42.147505Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

40 of 40 outbound references displayed

  • verified exact1
  • verified fuzzy23
  • unresolved16
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 381d8527-57aa-4f75-a087-8e52075f2bb7 · outbound

This paper cites The 17 Published as a workshop paper at ICLR 2025 training loss is the cross-entropy log-likelihood averaged along the sequence and averaged among all three domains.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation The 17 Published as a workshop paper at ICLR 2025 training loss is the cross-entropy log-likelihood averaged along the sequence and averaged among all three domains

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation ef2ee938-3323-4b0f-bb3b-d1964d51969c · outbound

This paper cites 5, we provide the ROC-AUC curves for all the combinations of Python, Java, and Go source and target languages in Fig.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation 5, we provide the ROC-AUC curves for all the combinations of Python, Java, and Go source and target languages in Fig

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 33e32293-7f18-4029-8848-02c3535ca2fb · outbound

This paper cites an unresolved cited work.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation Unresolved cited work

Reference 3

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation a2214373-723b-41ab-992b-5ffefdfc3e0d · outbound

This paper cites document.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation document

Reference 5

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-16T10:29:42.111756Z digest=sha256:916af5b7e0cf4b7658bc2f870eb4e535b556fa1abb3125864e02c1d75e5ab285

Observation 8e242ed7-3393-44c3-ae5f-82c7e3c95b9e · outbound

This paper cites Accessed: 2024-11-11.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation Accessed: 2024-11-11

Reference 6

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-16T10:29:42.021836Z digest=sha256:e120389891ada7579e3b25862f3fe1e358d87a934f40a9476b96819a743d6a7e

Observation 241e53d9-af63-4f13-8e06-61ecaec47517 · outbound

This paper cites Mixture-of-loras: An effi- cient multitask tuning method for large language models.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation Mixture-of-loras: An effi- cient multitask tuning method for large language models

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-16T10:29:42.025492Z digest=sha256:56b2e98d937721d13c84f1b046f4f8e4abf1981da8ce027b1ee6367af37b5263

Observation 3467d240-f94f-44bd-a0df-922404e27dcb · outbound

This paper cites Accessed: 2024- 12-05.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation Accessed: 2024- 12-05

Reference 8

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-16T10:29:42.029315Z digest=sha256:3bb7506829bfb637c41953dc66646eae8a92a41e3f2d00ff4bd78e64f5fbc842

Observation d45608a1-2112-40a6-afbf-862f056c200a · outbound

This paper cites Ac- cessed: 2024-11-11.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation Ac- cessed: 2024-11-11

Reference 9

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-16T10:29:42.033327Z digest=sha256:a29668e0769e16945df696c18f72c607b3af0cce5a8bab6de0cb4ae724d77c4f

Observation a66b9154-9855-4785-9225-17edaf1520c4 · outbound

This paper cites MultiCoder: Multi-Programming-Lingual Pre-Training for Low-Resource Code Completion.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation MultiCoder: Multi-Programming-Lingual Pre-Training for Low-Resource Code Completion

Reference 10

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source=pdf_text observed=2026-08-16T10:29:42.037375Z digest=sha256:7ad0805dad788d0c987b8202b395420e5816b4a00dc4e53aa0522a6c9c233797

Observation 60c59456-382c-4e81-8a2b-454e9aa32f1d · outbound

This paper cites CodeSearchNet Challenge: Evaluating the State of Semantic Code Search.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation CodeSearchNet Challenge: Evaluating the State of Semantic Code Search

Reference 11

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source=pdf_text observed=2026-08-16T10:29:42.041592Z digest=sha256:fdad91742b2701336aeeb07cd29cbfe1b3a158b9f91fc29d105b6fe44dca2707

Observation 409e7449-871d-41bf-9caf-1c1701119427 · outbound

This paper cites Worldwide Federated Training of Language Models.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation Worldwide Federated Training of Language Models

Reference 12

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source=pdf_text observed=2026-08-16T10:29:42.046004Z digest=sha256:b87b95e47483993df2227251147eafc03e8958ded4352f8c2d0b8b50873652ae

Observation 3efda4ca-dd85-4c2a-b9ea-04b74a0a5a07 · outbound

This paper cites Scaling Laws for Neural Language Models.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation Scaling Laws for Neural Language Models

Reference 13

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source=pdf_text observed=2026-08-16T10:29:42.049647Z digest=sha256:b7905403ee3eb46bc181230a0418ac838a5efc3f6c32fbbabb31c3e4d57be014

Observation 67176521-5a1f-4f30-bafa-4639cf51879f · outbound

This paper cites Hints-In-Browser: Benchmarking Language Models for Programming Feedback Generation.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation Hints-In-Browser: Benchmarking Language Models for Programming Feedback Generation

Reference 15

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-16T10:29:42.057621Z digest=sha256:458c431156d1eeff61a312bddc9c6442ea6e8c0377358f2d33733f8d5a8e1cee

Observation b697b479-8917-4232-ab70-d372f7e0c462 · outbound

This paper cites GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Reference 16

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source=pdf_text observed=2026-08-16T10:29:42.061768Z digest=sha256:5d014ef90ec2eed968fe12eed3ddd53bbc18677d2ef7da5ebffa6bbc4ed5720c

Observation ae0d6788-af29-4c06-a281-2ec4b0535410 · outbound

This paper cites CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation

Reference 18

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Observation ab7643d9-8518-4885-85fb-020c60793d90 · outbound

This paper cites Accessed: 2025-01-26.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation Accessed: 2025-01-26

Reference 21

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-16T10:29:42.081820Z digest=sha256:7739b5da73795fbc5475a89cf729e8003e42ab6be0329c00a7ffe9b02ce3d97e

Observation f76f9e27-16c4-43f3-b270-101276647d70 · outbound

This paper cites Accessed: 2024-12-05.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation Accessed: 2024-12-05

Reference 22

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-16T10:29:42.085614Z digest=sha256:f76d03116451ad96f1993b46252ad9884a9824132fee8060d80c564b0c17ecf2

Observation c8ec0a3b-eb01-498e-93fe-e349aff06d30 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation LLaMA: Open and Efficient Foundation Language Models

Reference 23

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source=pdf_text observed=2026-08-16T10:29:42.088652Z digest=sha256:a5b8e7533eb59fa2139ebd2e971bfd2322ccb11d452e553a27e7ca1be1ead4ca

Observation 4e4f63dd-6dcc-4591-a06b-f2a8de502ae4 · outbound

This paper cites Opacus: User-Friendly Differential Privacy Library in PyTorch.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 24

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Observation cb203baa-88d9-4ec0-b631-0ef5d575783d · outbound

This paper cites MoE-LPR: Multilingual Extension of Large Language Models through Mixture-of-Experts with Language Priors Routing.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation MoE-LPR: Multilingual Extension of Large Language Models through Mixture-of-Experts with Language Priors Routing

Reference 25

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Observation 3cf4e190-0b06-4dbd-9cea-c89f8b2de912 · outbound

This paper cites Universal and Transferable Adversarial Attacks on Aligned Language Models.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 26

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Observation 0e200bb9-7207-4874-9c98-87fbcc38278e · outbound

This paper cites However, this is an important problem with many additional dimensions to consider.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation However, this is an important problem with many additional dimensions to consider

Reference 27

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Observation 877ebf37-fc68-42c7-8c3b-d97347f0b96b · outbound

This paper cites We aim to explore such alternative forms for parameter-efficient adapters and their interplay with NoE in future work.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation We aim to explore such alternative forms for parameter-efficient adapters and their interplay with NoE in future work

Reference 28

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Observation 638bcd81-9b2e-4fe6-b21f-64e3fe57125b · outbound

This paper cites While not in the direct scope of our study, we do acknowledge their importance in various deployment settings and future avenues of work.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation While not in the direct scope of our study, we do acknowledge their importance in various deployment settings and future avenues of work

Reference 29

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Observation 43df15ec-dd72-4d0f-afd4-7301b5ba5d7e · outbound

This paper cites Domain Source Description Training Set Evaluation Set Pre-training GitHub Code (CodeParrot,.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation Domain Source Description Training Set Evaluation Set Pre-training GitHub Code (CodeParrot,

Reference 31

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Observation f46f54e3-47b4-449c-b37d-631fb9dee8f7 · outbound

This paper cites document.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation document

Reference 32

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source=pdf_text observed=2026-08-16T10:29:42.118940Z digest=sha256:ade7ab993b406190c5e5c0207ed026cc173090d193626a5f57d6396f6480c311

Observation 1dd93962-1397-4f2f-82d9-338411c396fa · outbound

This paper cites shared parameters.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation shared parameters

Reference 34

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Observation 85284dca-a863-4323-a16c-f8ce3150922b · outbound

This paper cites A small sweep of learning rates in Table 7 shows that either a smaller or larger learning rate results in lower accuracy.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation A small sweep of learning rates in Table 7 shows that either a smaller or larger learning rate results in lower accuracy

Reference 36

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Observation 0cdf99c2-0f08-4d50-bed7-d3ff62c2a27d · outbound

This paper cites The dataset is augmented with domain labels, which are used for deterministic routing.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation The dataset is augmented with domain labels, which are used for deterministic routing

Reference 38

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source=pdf_text observed=2026-08-16T10:29:42.140548Z digest=sha256:6db2600033a05b94d246682ee44fcd632e82e84bc3e803b24d26b8f1c1dddedd

Observation 8cc547c0-d54f-4710-893b-856db2a652a7 · outbound

This paper cites Setting [.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation Setting [

Reference 40

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-16T10:29:42.147505Z digest=sha256:eaf52c169638bdb16c150d8943d28ed7fef7e585ea41da5484b74342976cbf63

Observation bd9b16af-3566-4185-9895-df7528afb4ef · outbound

This paper cites below average.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation below average

Reference 2013

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-16T10:29:42.122838Z digest=sha256:8d5c2de32d7aa54e895a5d18ad0fe2220ae23966bbeac67b747a60f426513172

Observation 37826021-58ba-4cc0-8aad-bfbcdaa8d143 · outbound

This paper cites Regulation (eu) 2024/1689,.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation Regulation (eu) 2024/1689,

Reference 2014

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raw_fallback, observed 2026-08-16T10:29:42.599836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-16T10:29:42.018320Z digest=sha256:0729f6d714294b4db5664b85b20ae979c555d25158302028fea51bf388f6dda3

Observation d49c7b58-f1cb-4af6-8c4a-0a17c5188107 · outbound

This paper cites Peters, Swabha Swayamdipta, and Thomas Wolf.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation Peters, Swabha Swayamdipta, and Thomas Wolf

Reference 2016

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raw_fallback, observed 2026-08-16T10:29:42.536895Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-16T10:29:42.078029Z digest=sha256:c09825888176cb999887dc16517a42852f8620e0a39b16ec2d43b4738a5fee61

Observation 90b27a9d-da01-449c-bfae-2fbfd6e0c738 · outbound

This paper cites A Survey on Mixture of Experts in Large Language Models.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation A Survey on Mixture of Experts in Large Language Models

Reference 2019

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:29:42.002978Z digest=sha256:30db59f35aa6288758ac2375177c6bd74681dc0908f3962a929d8d979c79a382

Observation d88fff6a-6f3d-4336-ad8c-335399d2975f · outbound

This paper cites Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Reference 2020

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 2a507760-81cd-4583-ad9e-aabc97859210 · outbound

This paper cites MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-16T10:29:42.065762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 0d97a4bc-f923-43fe-bdc7-7fb17eb0c592 · outbound

This paper cites Context-aware membership inference attacks against pre-trained large language models.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation Context-aware membership inference attacks against pre-trained large language models

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-16T10:29:42.007536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 0bf8c1e4-62ab-4a47-9c34-2feff7c15603 · outbound

This paper cites com/index/gpt-3-5-turbo-fine-tuning-and-api-updates/.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation com/index/gpt-3-5-turbo-fine-tuning-and-api-updates/

Reference 2023

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T10:29:42.547416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 8f791be0-0c27-4e8f-929a-c6e287c4a3e1 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation Evaluating Large Language Models Trained on Code

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-16T10:29:42.011018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 34dfb10b-d669-4418-94a5-24045ef05fb4 · outbound

This paper cites Accessed: 2025-01-09.

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation Accessed: 2025-01-09

Reference 2025

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T10:29:42.610484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Pith citing papers

No inbound Pith citation observations are available.