Paper Citation Record · LEDGER

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning

As of 7 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 3 inbound Pith citation observations for arXiv:2506.19262.

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

pith.paper-citation-record.v1
2506.19262 v2

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:12:19.055117Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T15:53:39.113934Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

41 of 41 outbound references displayed

verified exact3
verified fuzzy6
unresolved32
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 645864b0-6078-460d-894c-0f73a24b1c66 · outbound

This paper cites Phi-4 Technical Report.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning Phi-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:18.851748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bba064c8-de37-4c7e-84c8-f3930b16b45b · outbound

This paper cites GPT-4 Technical Report.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning GPT-4 Technical Report

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:18.857973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7043876b-332c-475d-a4fb-22d500c877cd · outbound

This paper cites Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:18.863690Z

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Observation 6492b30d-3ec5-4f39-a21b-099f9cb35650 · outbound

This paper cites Large Language Models Suffer From Their Own Output: An Analysis of the Self-Consuming Training Loop.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning Large Language Models Suffer From Their Own Output: An Analysis of the Self-Consuming Training Loop

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:18.868972Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6c58840-c707-4615-bd4a-26565c4b61e0 · outbound

This paper cites Language GANs Falling Short.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning Language GANs Falling Short

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:18.875688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89491718-442f-463f-aaa7-146c097564c0 · outbound

This paper cites On the Diversity of Synthetic Data and its Impact on Training Large Language Models.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning On the Diversity of Synthetic Data and its Impact on Training Large Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:18.881689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5567f7ba-0961-48e8-9080-1a02a7cf09ed · outbound

This paper cites Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:18.888836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 505260d3-412a-48a7-a24d-6128c7fccdf9 · outbound

This paper cites AugGPT: Leveraging ChatGPT for Text Data Augmentation.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning AugGPT: Leveraging ChatGPT for Text Data Augmentation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:18.893945Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0df68e44-eaa5-4ce3-a3f6-002123cd2fe3 · outbound

This paper cites Universality of the π2/6 pathway in avoiding model collapse, 2024.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning Universality of the π2/6 pathway in avoiding model collapse, 2024

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:19.787288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 42019cdb-260c-4c0f-a28c-833ec6c747c1 · outbound

This paper cites Is GPT-3 a Good Data Annotator?.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning Is GPT-3 a Good Data Annotator?

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:18.904149Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c071fe3-c614-4553-ba3d-e2f9dc40d8b9 · outbound

This paper cites Data augmentation using llms: Data perspectives, learning paradigms and challenges.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning Data augmentation using llms: Data perspectives, learning paradigms and challenges

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:19.769259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e53f2ab7-2b3f-4ea7-98ae-47bb7a5f405d · outbound

This paper cites Model Collapse Demystified: The Case of Regression.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning Model Collapse Demystified: The Case of Regression

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:18.914754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bcb2006-a0a3-4586-9a10-828463c2288e · outbound

This paper cites Strong Model Collapse.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning Strong Model Collapse

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:18.920822Z

Source-reported events for the cited work

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Observation fd1d13bd-5531-4d84-ac15-50c6891f3cbe · outbound

This paper cites A Tale of Tails: Model Collapse as a Change of Scaling Laws.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning A Tale of Tails: Model Collapse as a Change of Scaling Laws

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:18.925506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0e572d1-5b86-4daa-95cb-0c81f392a9c2 · outbound

This paper cites The Llama 3 Herd of Models.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning The Llama 3 Herd of Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:18.930500Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a98b15e-7d2e-4c28-b846-013385ff65ff · outbound

This paper cites Beyond Model Collapse: Scaling Up with Synthesized Data Requires Verification.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning Beyond Model Collapse: Scaling Up with Synthesized Data Requires Verification

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:18.934682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a81f5911-b703-410c-8c4a-ea02c64c4425 · outbound

This paper cites Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:18.939264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1991e3be-c794-4a86-925d-05e592661c07 · outbound

This paper cites Chatgpt outperforms crowd workers for text-annotation tasks.Proceedings of the National Academy of Sciences, 120(30):e2305016120, 2023.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning Chatgpt outperforms crowd workers for text-annotation tasks.Proceedings of the National Academy of Sciences, 120(30):e2305016120, 2023

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:18.943859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d43b8f4d-bb3b-462c-8989-9ac16ce16723 · outbound

This paper cites The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic Text.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic Text

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:18.948737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7322882-a784-449e-93f6-5e77106b91ab · outbound

This paper cites TarGEN: Targeted Data Generation with Large Language Models.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning TarGEN: Targeted Data Generation with Large Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:18.953175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7de1fa9a-a33c-4f24-8c98-16f7edaedbb3 · outbound

This paper cites Collapse or Thrive? Perils and Promises of Synthetic Data in a Self-Generating World.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning Collapse or Thrive? Perils and Promises of Synthetic Data in a Self-Generating World

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:18.957552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 551809de-a8ab-45bf-a7ed-46802623ffbc · outbound

This paper cites The narrativeqa reading comprehension challenge, 2017.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning The narrativeqa reading comprehension challenge, 2017

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:18.962396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00dbaa3d-0fe8-48ec-ad14-e4843330d71f · outbound

This paper cites Not All LLM-Generated Data Are Equal: Rethinking Data Weighting in Text Classification.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning Not All LLM-Generated Data Are Equal: Rethinking Data Weighting in Text Classification

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:12:19.334959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7c60ce21-922e-463b-afa8-1cbbd6de9982 · outbound

This paper cites A diversity- promoting objective function for neural conversation models.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning A diversity- promoting objective function for neural conversation models

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:19.732046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5c87fad9-1675-4c54-a5c8-50224cb584aa · outbound

This paper cites Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:18.977169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a74c14c8-a7b2-4817-a577-3595866ff611 · outbound

This paper cites API-guided Dataset Synthesis to Finetune Large Code Models.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning API-guided Dataset Synthesis to Finetune Large Code Models

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:12:19.293347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ec65d921-5081-405f-910d-cd222a216ae7 · outbound

This paper cites Generating training data with language models: Towards zero-shot language understanding.Advances in Neural Information Processing Systems, 35:462–477, 2022.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning Generating training data with language models: Towards zero-shot language understanding.Advances in Neural Information Processing Systems, 35:462–477, 2022

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:19.716531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 04faf82d-35c8-4fb2-95f5-f385cf7333e1 · outbound

This paper cites A corpus and cloze evaluation for deeper understanding of commonsense stories.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning A corpus and cloze evaluation for deeper understanding of commonsense stories

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:18.990942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8b62230-41bd-4f64-ad14-816ffc6addee · outbound

This paper cites I learn better if you speak my language: Understanding the superior performance of fine-tuning large language models with llm-generated responses.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning I learn better if you speak my language: Understanding the superior performance of fine-tuning large language models with llm-generated responses

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:19.685719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9e77a416-a27a-4afa-88bf-749bc2797c13 · outbound

This paper cites How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:18.999831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7562b1ae-da84-49a0-98c6-5009f362cd28 · outbound

This paper cites The Curse of Recursion: Training on Generated Data Makes Models Forget.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning The Curse of Recursion: Training on Generated Data Makes Models Forget

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:19.004646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8ed6bdd-5545-402b-8ab4-7adc93da7581 · outbound

This paper cites Ai models collapse when trained on recursively generated data.Nature, 631(8022):755– 759, 2024.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning Ai models collapse when trained on recursively generated data.Nature, 631(8022):755– 759, 2024

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:19.009713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b67ba55-54a6-4052-b681-92f2c2ea78dd · outbound

This paper cites Large Language Models for Data Annotation and Synthesis: A Survey.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning Large Language Models for Data Annotation and Synthesis: A Survey

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:19.014617Z

Source-reported events for the cited work

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Observation ecf7367b-4479-42d4-a809-59f21da280f5 · outbound

This paper cites Evaluating the Evaluation of Diversity in Natural Language Generation.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning Evaluating the Evaluation of Diversity in Natural Language Generation

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:12:19.211776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1b8f8fe9-b838-442d-9eca-3a0233a5f284 · outbound

This paper cites Self-Instruct: Aligning Language Models with Self-Generated Instructions.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning Self-Instruct: Aligning Language Models with Self-Generated Instructions

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:19.026402Z

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Unavailable: canonical work link unavailable.

Observation e757d0cb-d62c-4142-8bc9-97b74cad6e9e · outbound

This paper cites CodecLM: Aligning Language Models with Tailored Synthetic Data.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning CodecLM: Aligning Language Models with Tailored Synthetic Data

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:19.031822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67fb0533-6b1d-479a-83cd-02341d3088a7 · outbound

This paper cites ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:19.036726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d3c46d2-027f-47ac-8757-7cd61c6761b9 · outbound

This paper cites ZeroGen: Efficient Zero-shot Learning via Dataset Generation.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning ZeroGen: Efficient Zero-shot Learning via Dataset Generation

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:19.041283Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19c461a0-09a6-4266-98e1-70345fc5d622 · outbound

This paper cites GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:19.045722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8804a4a2-3eb3-4378-9620-e07eea7c5218 · outbound

This paper cites Large language model as attributed training data generator: A tale of diversity and bias.Advances in Neural Information Processing Systems, 36, 2024.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning Large language model as attributed training data generator: A tale of diversity and bias.Advances in Neural Information Processing Systems, 36, 2024

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:19.654995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 82b574e1-7c95-4cdf-9616-d733186d0f80 · outbound

This paper cites How to Synthesize Text Data without Model Collapse?.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning How to Synthesize Text Data without Model Collapse?

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:19.055117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation f400b560-54ac-4a6e-a286-d40eb55b50fc · inbound

One Joke to Rule them All? On the (Im)possibility of Generalizing Humor cites this paper.

One Joke to Rule them All? On the (Im)possibility of Generalizing Humor What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-05T15:53:39.113934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f76b29ef-44c0-4475-9f5d-890516a762cf · inbound

Epistemic diversity across language models mitigates knowledge collapse cites this paper.

Epistemic diversity across language models mitigates knowledge collapse What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-03T15:59:04.798474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 41c68f9a-9d4c-4b38-ae8d-500c2ce2c6f5 · inbound

Less is Enough: Synthesizing Diverse Data in LLM Feature Space with Sparse Autoencoders cites this paper.

Less is Enough: Synthesizing Diverse Data in LLM Feature Space with Sparse Autoencoders What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T01:17:11.723762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.