Paper Citation Record · LEDGER

Best Practices and Lessons Learned on Synthetic Data

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 31 inbound Pith citation observations for arXiv:2404.07503.

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

pith.paper-citation-record.v1
2404.07503 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 31 of 31 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 31 of 31 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T17:09:15.329025Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T10:45:42.828782Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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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 d7eb1bc5-075d-4d90-82ab-1e276744f12d · inbound

A Survey on Large Language Models for Code Generation cites this paper.

A Survey on Large Language Models for Code Generation Best Practices and Lessons Learned on Synthetic Data

Reference 169

Resolution: verified exact; arxiv_id, observed 2026-05-13T20:18:06.427232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 261ecafe-d14b-4ecb-84d0-41aea6fa43a1 · inbound

Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing cites this paper.

Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing Best Practices and Lessons Learned on Synthetic Data

Reference 128

Resolution: verified exact; arxiv_id, observed 2026-05-16T06:58:36.878570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bebb443b-c626-4584-899b-6b9563634354 · inbound

Preserving Knowledge in Large Language Model with Model-Agnostic Self-Decompression cites this paper.

Preserving Knowledge in Large Language Model with Model-Agnostic Self-Decompression Best Practices and Lessons Learned on Synthetic Data

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-24T00:13:39.570150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 20642396-21ef-44a1-8752-3013428f0289 · inbound

DataComp-LM: In search of the next generation of training sets for language models cites this paper.

DataComp-LM: In search of the next generation of training sets for language models Best Practices and Lessons Learned on Synthetic Data

Reference 112

Resolution: verified exact; arxiv_id, observed 2026-05-17T22:58:17.079314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9cfff878-348a-4cfd-80de-b6f6a6108cd5 · inbound

From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap cites this paper.

From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap Best Practices and Lessons Learned on Synthetic Data

Reference 72

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:08:20.993624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e2642097-6eaa-4f1f-8d7f-4d59f3e21fe3 · inbound

Creating Artificial Students that Never Existed: Leveraging Large Language Models and CTGANs for Synthetic Data Generation cites this paper.

Creating Artificial Students that Never Existed: Leveraging Large Language Models and CTGANs for Synthetic Data Generation Best Practices and Lessons Learned on Synthetic Data

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-23T06:05:28.024455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 68491292-2250-48ad-85d4-02cb81a42492 · inbound

BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation cites this paper.

BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation Best Practices and Lessons Learned on Synthetic Data

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T17:09:15.329025Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7fa075e-3c7c-4373-b06a-e6fa883747f7 · inbound

Salamandra Technical Report cites this paper.

Salamandra Technical Report Best Practices and Lessons Learned on Synthetic Data

Reference 114

Resolution: unresolved; no resolver link, observed 2026-08-08T04:58:33.117933Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 837b83a5-c2c9-4ea6-8196-13835c6af893 · inbound

Generative AI at the Crossroads: Light Bulb, Dynamo, or Microscope? cites this paper.

Generative AI at the Crossroads: Light Bulb, Dynamo, or Microscope? Best Practices and Lessons Learned on Synthetic Data

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:15.347594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8edad706-d3c8-40b0-8ebf-6afbea63334e · inbound

Two Experts Are All You Need for Steering Thinking: Reinforcing Cognitive Effort in MoE Reasoning Models Without Additional Training cites this paper.

Two Experts Are All You Need for Steering Thinking: Reinforcing Cognitive Effort in MoE Reasoning Models Without Additional Training Best Practices and Lessons Learned on Synthetic Data

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:06.398779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14816447-f44a-4097-ae22-4ff0facda5dc · inbound

OrgAccess: A Benchmark for Role Based Access Control in Organization Scale LLMs cites this paper.

OrgAccess: A Benchmark for Role Based Access Control in Organization Scale LLMs Best Practices and Lessons Learned on Synthetic Data

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T14:22:07.612561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f8b9035-31b8-482b-abc4-c5df9f956dd4 · inbound

GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation cites this paper.

GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation Best Practices and Lessons Learned on Synthetic Data

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T14:00:31.405117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d84f3036-e691-4941-86c6-bafdc213e8b4 · inbound

EasyDistill: A Comprehensive Toolkit for Effective Knowledge Distillation of Large Language Models cites this paper.

EasyDistill: A Comprehensive Toolkit for Effective Knowledge Distillation of Large Language Models Best Practices and Lessons Learned on Synthetic Data

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T13:49:11.631956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 854ed8cf-0fff-412e-98e4-54cae89a945b · inbound

Seed-Coder: Let the Code Model Curate Data for Itself cites this paper.

Seed-Coder: Let the Code Model Curate Data for Itself Best Practices and Lessons Learned on Synthetic Data

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T11:04:56.812329Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42014936-3b41-4833-9fdf-6179158f65fc · inbound

How Far Are We from Generating Missing Modalities with Foundation Models? cites this paper.

How Far Are We from Generating Missing Modalities with Foundation Models? Best Practices and Lessons Learned on Synthetic Data

Reference 66

Resolution: verified exact; arxiv_id, observed 2026-05-25T08:15:33.642335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 03d7a2d4-33e7-4be3-bc8f-9f1c87f87286 · inbound

Does Prompt Design Impact Quality of Data Imputation by LLMs? cites this paper.

Does Prompt Design Impact Quality of Data Imputation by LLMs? Best Practices and Lessons Learned on Synthetic Data

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:50.157269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a24b728d-e810-4d29-ba9b-85670ae75961 · inbound

No Data? No Problem: Synthesizing Security Graphs for Better Intrusion Detection cites this paper.

No Data? No Problem: Synthesizing Security Graphs for Better Intrusion Detection Best Practices and Lessons Learned on Synthetic Data

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-19T10:42:15.271569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 053bcf55-ffa2-4b46-80cf-249b35f1821b · inbound

Unlocking the Potential of Large Language Models in the Nuclear Industry with Synthetic Data cites this paper.

Unlocking the Potential of Large Language Models in the Nuclear Industry with Synthetic Data Best Practices and Lessons Learned on Synthetic Data

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:37.206170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00a2ab1c-5f9a-4576-877a-f79904ff2664 · inbound

Using Sign Language Production as Data Augmentation to enhance Sign Language Translation cites this paper.

Using Sign Language Production as Data Augmentation to enhance Sign Language Translation Best Practices and Lessons Learned on Synthetic Data

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T04:47:50.601837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd994a8a-6e91-4559-84f9-c47e756904ad · inbound

MDBench: A Synthetic Multi-Document Reasoning Benchmark Generated with Knowledge Guidance cites this paper.

MDBench: A Synthetic Multi-Document Reasoning Benchmark Generated with Knowledge Guidance Best Practices and Lessons Learned on Synthetic Data

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T00:14:06.839189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10f2024b-5644-4319-aa21-14e4442a5c3d · inbound

Enterprise Large Language Model Evaluation Benchmark cites this paper.

Enterprise Large Language Model Evaluation Benchmark Best Practices and Lessons Learned on Synthetic Data

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T22:56:32.969986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ccb77f5e-4dce-4b24-8c9b-29c95b3b5842 · inbound

A Penalty Goes a Long Way: Measuring Lexical Diversity in Synthetic Texts Under Prompt-Influenced Length Variations cites this paper.

A Penalty Goes a Long Way: Measuring Lexical Diversity in Synthetic Texts Under Prompt-Influenced Length Variations Best Practices and Lessons Learned on Synthetic Data

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T15:46:27.785294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f46116f-12e3-4f8d-87e8-dad14f0bbd48 · inbound

Synthetic CVs To Build and Test Fairness-Aware Hiring Tools cites this paper.

Synthetic CVs To Build and Test Fairness-Aware Hiring Tools Best Practices and Lessons Learned on Synthetic Data

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-05T14:35:15.854794Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fde7a88d-f3d0-4a50-96d7-5813628ee26f · inbound

CultureSynth: A Hierarchical Taxonomy-Guided and Retrieval-Augmented Framework for Cultural Question-Answer Synthesis cites this paper.

CultureSynth: A Hierarchical Taxonomy-Guided and Retrieval-Augmented Framework for Cultural Question-Answer Synthesis Best Practices and Lessons Learned on Synthetic Data

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T17:31:39.851872Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b375df1-c1e2-4208-96cc-4ef574b186e1 · inbound

Multi-Model Synthetic Training for Mission-Critical Small Language Models cites this paper.

Multi-Model Synthetic Training for Mission-Critical Small Language Models Best Practices and Lessons Learned on Synthetic Data

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-18T15:51:34.970107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2342313d-bf17-4930-b6ab-0e6f1e714021 · inbound

Synthetic Interaction Data for Scalable Personalization in Large Language Models cites this paper.

Synthetic Interaction Data for Scalable Personalization in Large Language Models Best Practices and Lessons Learned on Synthetic Data

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-02T23:53:03.578108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c9618e1-8188-4c1c-9c28-8244136eb22d · inbound

OceanPile: A Large-Scale Multimodal Ocean Corpus for Foundation Models cites this paper.

OceanPile: A Large-Scale Multimodal Ocean Corpus for Foundation Models Best Practices and Lessons Learned on Synthetic Data

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:56:06.905377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6428d66e-ac53-4858-9dbc-7efc1dba292d · inbound

Synthetic Data from Cross-Domain Events for Large-Scale Recommendation Systems cites this paper.

Synthetic Data from Cross-Domain Events for Large-Scale Recommendation Systems Best Practices and Lessons Learned on Synthetic Data

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T20:42:37.295434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b4858c3c-ce57-4a6a-b6e6-19785dc2e6f3 · inbound

Self-Study Reconsidered: The Hidden Fragility of Learning from Self-Generated QA cites this paper.

Self-Study Reconsidered: The Hidden Fragility of Learning from Self-Generated QA Best Practices and Lessons Learned on Synthetic Data

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-07-01T10:45:42.830358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 089135ac-b305-46c8-96e7-626fd552b4c3 · inbound

Search Hardness-Aware LLM-Based Problem Formulation for Expensive Simulation-Driven Design cites this paper.

Search Hardness-Aware LLM-Based Problem Formulation for Expensive Simulation-Driven Design Best Practices and Lessons Learned on Synthetic Data

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T08:09:44.978551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d82f85f4-9f00-4a59-90ce-80f0f285d8d9 · inbound

DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data cites this paper.

DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data Best Practices and Lessons Learned on Synthetic Data

Reference 42

Resolution: unresolved; no resolver link, observed 2026-07-31T07:01:46.557635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.