Paper Citation Record · LEDGER

Approximating Language Model Training Data from Weights

As of 10 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2506.15553.

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

pith.paper-citation-record.v1
2506.15553 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:59:04.660958Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-03T17:54:31.856386Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T17:58:46.704232Z

Reference resolution

51 of 51 outbound references displayed

verified exact8
verified fuzzy7
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fdfa3b40-b820-4042-98e4-bace57be755b · outbound

This paper cites Dbpedia: A nucleus for a web of open data, 2007.

Approximating Language Model Training Data from Weights Dbpedia: A nucleus for a web of open data, 2007

Reference 1

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Observation fbf335a2-7b14-4a0d-8c7d-e41ea3bc3db0 · outbound

This paper cites MS MARCO: A Human Generated MAchine Reading COmprehension Dataset.

Approximating Language Model Training Data from Weights MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T23:58:58.012122Z

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Observation d5872ccb-fdce-452b-96e9-d7e21300b18f · outbound

This paper cites Reconstructing Training Data with Informed Adversaries.

Approximating Language Model Training Data from Weights Reconstructing Training Data with Informed Adversaries

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:59:07.808516Z

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Observation 882d2219-1c48-4406-b242-62e33507d74b · outbound

This paper cites Coresets via bilevel optimization for continual learning and streaming.

Approximating Language Model Training Data from Weights Coresets via bilevel optimization for continual learning and streaming

Reference 4

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Observation d6171dfb-ab40-4209-8f4d-a2f1c58727ca · outbound

This paper cites Deconstructing Data Reconstruction: Multiclass, Weight Decay and General Losses.

Approximating Language Model Training Data from Weights Deconstructing Data Reconstruction: Multiclass, Weight Decay and General Losses

Reference 5

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Observation 63487873-dc3e-4722-88fe-87cac685c658 · outbound

This paper cites Extracting Training Data from Large Language Models.

Approximating Language Model Training Data from Weights Extracting Training Data from Large Language Models

Reference 6

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Observation 0517969b-9f8a-4e21-a65e-6da80e19aef2 · outbound

This paper cites Quantifying memorization across neural language models.

Approximating Language Model Training Data from Weights Quantifying memorization across neural language models

Reference 7

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Source-reported events for the cited work

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Observation 381bac5a-b44b-42a5-9686-fd745ce10bd0 · outbound

This paper cites Stealing Part of a Production Language Model.

Approximating Language Model Training Data from Weights Stealing Part of a Production Language Model

Reference 8

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Observation 2e1c84ec-1c7a-4119-b983-1c025c8a6116 · outbound

This paper cites Dataset Distillation by Matching Training Trajectories.

Approximating Language Model Training Data from Weights Dataset Distillation by Matching Training Trajectories

Reference 9

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Observation 5b4e2cf0-fa80-4443-9f72-af8a22f73c3c · outbound

This paper cites Super-Samples from Kernel Herding.

Approximating Language Model Training Data from Weights Super-Samples from Kernel Herding

Reference 10

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Observation 69f3cd9f-f318-4d54-8ef4-c16c60d02c69 · outbound

This paper cites Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory.

Approximating Language Model Training Data from Weights Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory

Reference 11

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Observation 335db460-6d08-44d7-be94-c37bd35296d1 · outbound

This paper cites Sinkhorn distances: Lightspeed computation of optimal transport.

Approximating Language Model Training Data from Weights Sinkhorn distances: Lightspeed computation of optimal transport

Reference 12

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Observation 280a3a48-41d0-4ebc-967b-4e9ef647e72e · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Approximating Language Model Training Data from Weights DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 13

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Observation 4de0d262-14d4-461d-9963-64712db86c6f · outbound

This paper cites DsDm: Model-Aware Dataset Selection with Datamodels.

Approximating Language Model Training Data from Weights DsDm: Model-Aware Dataset Selection with Datamodels

Reference 14

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Observation 049658bc-669e-46b3-bf0c-cbd2f98e67e4 · outbound

This paper cites Automatic Document Selection for Efficient Encoder Pretraining.

Approximating Language Model Training Data from Weights Automatic Document Selection for Efficient Encoder Pretraining

Reference 15

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Observation ae09eb1b-53dd-426a-9948-9b070d39cd72 · outbound

This paper cites Logits of API-Protected LLMs Leak Proprietary Information.

Approximating Language Model Training Data from Weights Logits of API-Protected LLMs Leak Proprietary Information

Reference 16

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Observation 848806e4-4cf2-41f8-a7a8-4bdebfd89c33 · outbound

This paper cites Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching.

Approximating Language Model Training Data from Weights Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching

Reference 17

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Observation 30567002-d23c-401b-bd37-c7434841a361 · outbound

This paper cites Reconstructing Training Data from Trained Neural Networks.

Approximating Language Model Training Data from Weights Reconstructing Training Data from Trained Neural Networks

Reference 18

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Observation 95c9a9b6-db3c-47f6-9ab2-acb73e48fa84 · outbound

This paper cites Can we infer confidential properties of training data from llms?, 2025.

Approximating Language Model Training Data from Weights Can we infer confidential properties of training data from llms?, 2025

Reference 19

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Observation 58f4fcca-546c-42cb-81b8-087e72d355d0 · outbound

This paper cites D-optimality for regression designs: a review.

Approximating Language Model Training Data from Weights D-optimality for regression designs: a review

Reference 20

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Observation 0e70ed92-8804-442b-b8dd-91421c4ce91f · outbound

This paper cites Johnson and Joram Lindenstrauss.

Approximating Language Model Training Data from Weights Johnson and Joram Lindenstrauss

Reference 21

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Observation 9718a7e9-6240-4b8d-9800-84734bcb4650 · outbound

This paper cites Grad-match: Gradient matching based data subset selection for efficient deep model training.

Approximating Language Model Training Data from Weights Grad-match: Gradient matching based data subset selection for efficient deep model training

Reference 22

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Observation ec40b26f-58ae-4bbb-ae88-64b6979cd733 · outbound

This paper cites Glister: Generalization based data subset selection for efficient and robust learning.

Approximating Language Model Training Data from Weights Glister: Generalization based data subset selection for efficient and robust learning

Reference 23

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Observation 5fe28127-6b58-442a-b10b-b5e21a07d82e · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Approximating Language Model Training Data from Weights Adam: A Method for Stochastic Optimization

Reference 24

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Observation 424fc9c5-e403-413d-a85c-c03467e1a799 · outbound

This paper cites From word embeddings to document distances.

Approximating Language Model Training Data from Weights From word embeddings to document distances

Reference 25

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Observation 7e87c0ad-7046-49ad-a2ed-624b11c00dbb · outbound

This paper cites Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov.

Approximating Language Model Training Data from Weights Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov

Reference 26

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Observation c3ba1c5e-50b3-4d04-9b84-57ac7e9733d8 · outbound

This paper cites Making Large Language Models Better Data Creators.

Approximating Language Model Training Data from Weights Making Large Language Models Better Data Creators

Reference 27

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Observation 71d640f6-0d1f-4b9b-ac11-125c19e8d0dc · outbound

This paper cites Large Language Models Can Be Strong Differentially Private Learners.

Approximating Language Model Training Data from Weights Large Language Models Can Be Strong Differentially Private Learners

Reference 28

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Observation 4ef1e7fe-f22b-4232-b7c6-0365d4fbf836 · outbound

This paper cites Efficient model development through fine-tuning transfer, 2025.

Approximating Language Model Training Data from Weights Efficient model development through fine-tuning transfer, 2025

Reference 29

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Observation f32a1e12-6f3f-4801-a977-3345b5a12a0a · outbound

This paper cites DeepSeek-V3 Technical Report.

Approximating Language Model Training Data from Weights DeepSeek-V3 Technical Report

Reference 30

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Observation 43d2f168-00cc-40c8-9e15-91fdfdb406be · outbound

This paper cites DiLM: Distilling Dataset into Language Model for Text-level Dataset Distillation.

Approximating Language Model Training Data from Weights DiLM: Distilling Dataset into Language Model for Text-level Dataset Distillation

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:59:05.548663Z

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Observation 7d54b2b6-e4cc-4351-a1fe-ea6513ac22c0 · outbound

This paper cites The Llama 3 Herd of Models.

Approximating Language Model Training Data from Weights The Llama 3 Herd of Models

Reference 32

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Observation cdc8614b-9460-4c68-8d0e-329cd8e5df60 · outbound

This paper cites Coresets for robust training of deep neural networks against noisy labels.

Approximating Language Model Training Data from Weights Coresets for robust training of deep neural networks against noisy labels

Reference 33

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Observation a9259e47-3708-4978-90d2-250f46b8bc60 · outbound

This paper cites Twenty Newsgroups.

Approximating Language Model Training Data from Weights Twenty Newsgroups

Reference 34

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Observation 67848785-9880-4729-8fba-e2a0548ab204 · outbound

This paper cites Language Model Inversion.

Approximating Language Model Training Data from Weights Language Model Inversion

Reference 35

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Observation 71335e31-71e3-47e8-84ca-71cfd9d979ce · outbound

This paper cites How much do language models memorize?.

Approximating Language Model Training Data from Weights How much do language models memorize?

Reference 36

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Observation db648e96-6e9c-4040-be16-00146c2ae2b6 · outbound

This paper cites Scalable Extraction of Training Data from (Production) Language Models.

Approximating Language Model Training Data from Weights Scalable Extraction of Training Data from (Production) Language Models

Reference 37

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Observation a8c24dff-b8b3-4395-900e-3ad2923ed06d · outbound

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Approximating Language Model Training Data from Weights Unresolved cited work

Reference 38

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Observation 5571f04f-54f5-4128-b128-4ada8f854d24 · outbound

This paper cites Synthetic Text Generation for Training Large Language Models via Gradient Matching.

Approximating Language Model Training Data from Weights Synthetic Text Generation for Training Large Language Models via Gradient Matching

Reference 39

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This paper cites Estimating training data influence by tracing gradient descent.

Approximating Language Model Training Data from Weights Estimating training data influence by tracing gradient descent

Reference 40

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Observation 3039758f-80b8-490c-a8ae-8429c7f967d7 · outbound

This paper cites Language models are unsupervised multitask learners.

Approximating Language Model Training Data from Weights Language models are unsupervised multitask learners

Reference 41

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Observation 91d15c3e-f10f-48b4-86a5-48b4bec7eb0c · outbound

This paper cites Training Data Reconstruction: Privacy due to Uncertainty?.

Approximating Language Model Training Data from Weights Training Data Reconstruction: Privacy due to Uncertainty?

Reference 42

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Observation 75cc5f64-cb2f-4299-8a0e-067d162f76e8 · outbound

This paper cites Dataset Distillation.

Approximating Language Model Training Data from Weights Dataset Distillation

Reference 43

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Observation 86930eeb-2581-4feb-8996-c825a2b0a21e · outbound

This paper cites LESS: Selecting Influential Data for Targeted Instruction Tuning.

Approximating Language Model Training Data from Weights LESS: Selecting Influential Data for Targeted Instruction Tuning

Reference 44

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Observation 5a1c75ae-a5d5-4da6-9577-fd123feecb0a · outbound

This paper cites Data selection for language models via importance resampling.

Approximating Language Model Training Data from Weights Data selection for language models via importance resampling

Reference 45

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Observation aecf3c81-523d-4fce-af6f-0980670db00f · outbound

This paper cites Compute-Constrained Data Selection.

Approximating Language Model Training Data from Weights Compute-Constrained Data Selection

Reference 46

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Observation a0944248-4c16-4dc6-8729-1a0cff764c0c · outbound

This paper cites Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective.

Approximating Language Model Training Data from Weights Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective

Reference 47

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Observation 3f63f595-2720-4c1e-a243-943ded2ecdff · outbound

This paper cites Character-level Convolutional Networks for Text Classification.

Approximating Language Model Training Data from Weights Character-level Convolutional Networks for Text Classification

Reference 48

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Observation 47ce09c5-d399-44c2-8303-d59882e02e31 · outbound

This paper cites Dataset Condensation with Gradient Matching.

Approximating Language Model Training Data from Weights Dataset Condensation with Gradient Matching

Reference 49

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Observation 3546edc5-6224-4098-a92a-d5a40d1de66c · outbound

This paper cites Dataset distillation using neural feature regression.

Approximating Language Model Training Data from Weights Dataset distillation using neural feature regression

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:59:08.070508Z

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Observation 20e572c3-a858-4cc0-86ba-c4522f835769 · outbound

This paper cites write newline.

Approximating Language Model Training Data from Weights write newline

Reference 51

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Pith citing papers

Observation 73d46ce7-8313-4b21-9bf5-ead3aaaaf5d2 · inbound

WARP: Weight-Space Analysis for Recovering Training Data Portfolios cites this paper.

WARP: Weight-Space Analysis for Recovering Training Data Portfolios Approximating Language Model Training Data from Weights

Reference 18

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