Paper Citation Record · LEDGER

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models

As of 18 August 2026, this Paper Citation Record lists 100 of 250 outbound references and 8 inbound Pith citation observations for arXiv:2412.02980.

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

pith.paper-citation-record.v1
2412.02980 v2

Coverage vector

measured 100 of 250 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T22:57:01.768492Z

measured 108 of 108 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:25:14.843945Z

measured 1 of 1 external citation measurements

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

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

Reference resolution

100 of 250 outbound references displayed

verified exact0
verified fuzzy0
unresolved100
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

4
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 2ca369a7-fbe5-4dfe-9944-7dad12e0af99 · outbound

This paper cites write newline.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.482320Z

Source-reported events for the cited work

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Observation c13b4df7-8aed-460a-86f3-6d9bd1998347 · outbound

This paper cites @esa (Ref.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models @esa (Ref

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.487766Z

Source-reported events for the cited work

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Observation a892d8d3-8cee-4d31-9dea-cc9f20ad253d · outbound

This paper cites an unresolved cited work.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Unresolved cited work

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.490993Z

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Observation 7cd26eab-6064-4aa5-81d2-691d3c23bbef · outbound

This paper cites and ``interesting.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models and ``interesting

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.493802Z

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Observation 7f083a30-2642-4441-b9aa-925f58a1d7c5 · outbound

This paper cites SemDeDup: Data-efficient learning at web-scale through semantic deduplication.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models SemDeDup: Data-efficient learning at web-scale through semantic deduplication

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.499375Z

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Observation 747c2bcd-fbb4-46e9-8c81-997a5b3eed86 · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.502803Z

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Observation 1f5d0bd3-c4de-4a2a-aa52-d76aebb4946e · outbound

This paper cites Knowledge graph based synthetic corpus generation for knowledge-enhanced language model pre-training.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Knowledge graph based synthetic corpus generation for knowledge-enhanced language model pre-training

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.506520Z

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Observation ae144ebf-c346-4247-9ce4-cee9e803ace0 · outbound

This paper cites Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.509683Z

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Observation a65ba5dd-1056-4949-8c0b-0b57eaba19aa · outbound

This paper cites Efficient online data mixing for language model pre-training.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Efficient online data mixing for language model pre-training

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.513009Z

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Observation 91530c31-3c06-48f5-a462-136afa3d4950 · outbound

This paper cites Improving few-shot generalization by exploring and exploiting auxiliary data.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Improving few-shot generalization by exploring and exploiting auxiliary data

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.516004Z

Source-reported events for the cited work

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Observation a81d3178-50f5-48c9-b16a-727521ad396c · outbound

This paper cites A Survey on Data Selection for Language Models.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models A Survey on Data Selection for Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.518663Z

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Observation b7aa2b78-6f4b-44dc-bb86-50b84738b03b · outbound

This paper cites Critique-out-Loud Reward Models.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Critique-out-Loud Reward Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.521247Z

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Observation 9ee600e0-4856-409d-9537-7a5428855c18 · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.524028Z

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Observation fa077800-dfc4-405d-bf45-fbc87e8caeb0 · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Constitutional AI: Harmlessness from AI Feedback

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.527354Z

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Observation 0b5c7929-0bae-4512-a413-a98f048578c0 · outbound

This paper cites Comprehensive Exploration of Synthetic Data Generation: A Survey.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Comprehensive Exploration of Synthetic Data Generation: A Survey

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.530403Z

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Observation bcd2e3d7-27e5-4217-a39b-d1daf2065adf · outbound

This paper cites Cosmopedia, February 2024.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Cosmopedia, February 2024

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.533152Z

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Observation 46859079-e847-476d-b5c8-c881a719ce9d · outbound

This paper cites an unresolved cited work.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Unresolved cited work

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.535517Z

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Observation a7e1d83e-b2f1-4af1-902f-be7ca3a97e01 · outbound

This paper cites Deep surrogate assisted generation of environments.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Deep surrogate assisted generation of environments

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.538043Z

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Observation b2556043-8934-4ea5-b430-9f9ab62573e2 · outbound

This paper cites ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.540311Z

Source-reported events for the cited work

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Observation c2e2f868-5835-4599-b660-6aa2f91ed240 · outbound

This paper cites Quality-Diversity through AI Feedback.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Quality-Diversity through AI Feedback

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.542835Z

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Observation b7237118-9a0f-49e4-a4b4-dccd62e61844 · outbound

This paper cites Large Language Monkeys: Scaling Inference Compute with Repeated Sampling.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.545371Z

Source-reported events for the cited work

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Observation df24554b-6a7b-47a2-a52f-368dfcf035de · outbound

This paper cites Language models are few-shot learners.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Language models are few-shot learners

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.548089Z

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Observation 0fc349bd-e9f3-431a-b5d9-d8f5915245cd · outbound

This paper cites Language Models are Few-Shot Learners.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Language Models are Few-Shot Learners

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.550889Z

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Observation 0f290ad2-10f8-43bb-8b6d-ddd3659857a2 · outbound

This paper cites Data Diversity Matters for Robust Instruction Tuning.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Data Diversity Matters for Robust Instruction Tuning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.553604Z

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Observation e9f24745-87f6-461d-96de-cfddffac80d5 · outbound

This paper cites Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.556302Z

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Observation f58c4426-27ed-40aa-8914-61e0532ec9fb · outbound

This paper cites Instruction Mining: Instruction Data Selection for Tuning Large Language Models.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.559066Z

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Observation 7aff04f3-0f19-4868-9400-7a4291f4f1a1 · outbound

This paper cites A Machine Learning Approach to Comment Toxicity Classification.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models A Machine Learning Approach to Comment Toxicity Classification

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.561721Z

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Observation a36fe951-d449-43c3-94aa-34d6ce902b20 · outbound

This paper cites Scaling Synthetic Data Creation with 1,000,000,000 Personas.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Scaling Synthetic Data Creation with 1,000,000,000 Personas

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.564680Z

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Observation 4e374418-b12c-4c3e-9343-dbfdc317b935 · outbound

This paper cites Quality-Diversity Generative Sampling for Learning with Synthetic Data.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Quality-Diversity Generative Sampling for Learning with Synthetic Data

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.567562Z

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Observation cc628f81-24a9-49e9-a667-494f4a6839c2 · outbound

This paper cites When Large Language Models Meet Evolutionary Algorithms: Potential Enhancements and Challenges.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models When Large Language Models Meet Evolutionary Algorithms: Potential Enhancements and Challenges

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.570539Z

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Observation 4afa5f68-d35f-4545-80b8-cfe3dbacc57a · outbound

This paper cites Quality-diversity optimization: a novel branch of stochastic optimization.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Quality-diversity optimization: a novel branch of stochastic optimization

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.573375Z

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Observation f647a0a2-19be-4b99-a646-61c71b727276 · outbound

This paper cites EvoPrompting: Language Models for Code-Level Neural Architecture Search.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models EvoPrompting: Language Models for Code-Level Neural Architecture Search

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.575642Z

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Observation 79a021ac-c605-4bd2-a971-f3ef6ab589fd · outbound

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

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models On the Diversity of Synthetic Data and its Impact on Training Large Language Models

Reference 33

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Observation a46fcc67-ed1f-4136-82ea-c631522f09f9 · outbound

This paper cites AlpaGasus: Training A Better Alpaca with Fewer Data.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models AlpaGasus: Training A Better Alpaca with Fewer Data

Reference 34

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Observation 6a7b69a9-3a39-41ae-86ba-2a379d29608d · outbound

This paper cites Skill-it! A Data-Driven Skills Framework for Understanding and Training Language Models.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Skill-it! A Data-Driven Skills Framework for Understanding and Training Language Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.583153Z

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Observation fbd7dd49-382c-4124-9013-666138e7347d · outbound

This paper cites Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks

Reference 36

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Observation 499bd87f-ee68-4e9e-8a3b-6bd532e9fe9f · outbound

This paper cites Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models

Reference 37

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Observation 9df11f52-52b9-474e-863d-ce810e8ac246 · outbound

This paper cites GenAug: Retargeting behaviors to unseen situations via Generative Augmentation.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models GenAug: Retargeting behaviors to unseen situations via Generative Augmentation

Reference 38

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Observation 0e84f979-b41c-4d41-868f-f52a7c48ec96 · outbound

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Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Gonzalez, Ion Stoica, and Eric P

Reference 39

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Observation a73503b5-e71e-4312-88ac-1774d42e6bbd · outbound

This paper cites Diversity-Rewarded CFG Distillation.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Diversity-Rewarded CFG Distillation

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.596170Z

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Observation 9157f503-a280-4968-a3b6-94a9078fae44 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Training Verifiers to Solve Math Word Problems

Reference 41

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Observation 6f509ba6-8617-4aaf-9f80-fd5b2a20975d · outbound

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Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Quality and diversity optimization: A unifying modular framework

Reference 42

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Observation ba317188-fac3-4cfa-9bd6-31fd3687cb90 · outbound

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Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Unresolved cited work

Reference 43

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Observation 85751ac7-0807-47fa-a81a-114b5adca09e · outbound

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

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Is GPT-3 a Good Data Annotator?

Reference 44

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Observation a215cac1-d840-4703-ba99-6eff333784dc · outbound

This paper cites Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven Optimization.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven Optimization

Reference 45

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Observation ac034c98-970f-4300-a76d-7bf9922331ce · outbound

This paper cites How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.613086Z

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Observation 83ce8e05-f2f9-4331-ab7a-aedceafe4f48 · outbound

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Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment

Reference 47

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Observation 627cb14b-9c02-4ba8-af9b-d8e0467d3879 · outbound

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Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models GLaM: Efficient Scaling of Language Models with Mixture-of-Experts

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.619639Z

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Observation 424ca5a5-5e2d-411a-ba4e-18f0017fd222 · outbound

This paper cites The Llama 3 Herd of Models.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models The Llama 3 Herd of Models

Reference 50

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Observation 1741d9bb-6a8b-4c68-b1f4-474d1bd0267f · outbound

This paper cites TinyStories: How Small Can Language Models Be and Still Speak Coherent English?.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models TinyStories: How Small Can Language Models Be and Still Speak Coherent English?

Reference 51

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Observation 07e4259e-4d95-4f78-9969-1f184bcf421d · outbound

This paper cites Understanding dataset difficulty with V -usable information.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Understanding dataset difficulty with V -usable information

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.631852Z

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Observation 77dab9f3-2543-4c49-a93d-8690774d3531 · outbound

This paper cites Scaling Laws of Synthetic Images for Model Training ... for Now.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Scaling Laws of Synthetic Images for Model Training ... for Now

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.634864Z

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Observation 7d772907-ce09-4d00-8749-dba835be5d5e · outbound

This paper cites Irreducible Curriculum for Language Model Pretraining.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Irreducible Curriculum for Language Model Pretraining

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.637548Z

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Observation d76f722c-6e36-40b6-ac00-de14e0c9267e · outbound

This paper cites Promptbreeder: Self-referential self-improvement via prompt evolution, 2023 a.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Promptbreeder: Self-referential self-improvement via prompt evolution, 2023 a

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.640034Z

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Observation 88f73475-dc91-4167-b578-16b4c9e04ebc · outbound

This paper cites Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.642298Z

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Observation ecc80338-7120-4599-bf41-01279bf3df9c · outbound

This paper cites A quality diversity approach to automatically generating human-robot interaction scenarios in shared autonomy.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models A quality diversity approach to automatically generating human-robot interaction scenarios in shared autonomy

Reference 57

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Observation bf916bd4-30ca-4a4b-9244-869e38c4b099 · outbound

This paper cites On the importance of environments in human-robot coordination.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models On the importance of environments in human-robot coordination

Reference 58

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This paper cites Illuminating Mario Scenes in the Latent Space of a Generative Adversarial Network.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Illuminating Mario Scenes in the Latent Space of a Generative Adversarial Network

Reference 59

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This paper cites Stream of Search (SoS): Learning to Search in Language.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Stream of Search (SoS): Learning to Search in Language

Reference 60

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Observation 034d0254-0f7a-42c8-b96b-b600df627392 · outbound

This paper cites Better Synthetic Data by Retrieving and Transforming Existing Datasets.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Better Synthetic Data by Retrieving and Transforming Existing Datasets

Reference 61

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Observation eeda1ad2-35b9-4727-8d21-4f822953af51 · outbound

This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 62

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Observation ad6fd7ed-313c-4616-a9fd-eedfb5c70edc · outbound

This paper cites CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing

Reference 63

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Observation 0fcbdf0a-ecf8-4007-a715-64f4390c38fc · outbound

This paper cites Automated Curriculum Learning for Neural Networks.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Automated Curriculum Learning for Neural Networks

Reference 64

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Observation 948ab8f3-5eab-4fa6-b5c8-81681024ed3a · outbound

This paper cites Reinforced Self-Training (ReST) for Language Modeling.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Reinforced Self-Training (ReST) for Language Modeling

Reference 65

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Observation 35d57db0-c7c2-4732-a3bc-812863b1f5ba · outbound

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Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Textbooks Are All You Need

Reference 66

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Observation 35415944-4c42-40ba-8726-d1034c241e57 · outbound

This paper cites Generative AI for Synthetic Data Generation: Methods, Challenges and the Future.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Generative AI for Synthetic Data Generation: Methods, Challenges and the Future

Reference 67

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Observation 92076b25-8751-43cd-9914-d591fcb38e4b · outbound

This paper cites Mastering Diverse Domains through World Models.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Mastering Diverse Domains through World Models

Reference 68

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Observation 8262b2bc-3063-465d-9c2e-a697dd650092 · outbound

This paper cites Unifying Human and Statistical Evaluation for Natural Language Generation.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Unifying Human and Statistical Evaluation for Natural Language Generation

Reference 69

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Observation c03b2c12-18c3-4c56-bc5e-946add09214a · outbound

This paper cites Understanding Scaling Laws with Statistical and Approximation Theory for Transformer Neural Networks on Intrinsically Low-dimensional Data.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Understanding Scaling Laws with Statistical and Approximation Theory for Transformer Neural Networks on Intrinsically Low-dimensional Data

Reference 70

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Observation cf63e4ed-bb18-479c-aa5d-50872c9e5c05 · outbound

This paper cites Teaching Large Language Models to Reason with Reinforcement Learning.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Teaching Large Language Models to Reason with Reinforcement Learning

Reference 71

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Observation acf75beb-9fe9-411e-a2a2-7c781bf8c757 · outbound

This paper cites GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements

Reference 72

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Observation 01d088df-9690-4bb2-ac26-6ca339dfe67b · outbound

This paper cites trl X : A framework for large scale reinforcement learning from human feedback.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models trl X : A framework for large scale reinforcement learning from human feedback

Reference 73

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Observation 6581e03e-2b94-4961-9357-d9cdf31563b9 · outbound

This paper cites Psychometric Alignment: Capturing Human Knowledge Distributions via Language Models.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Psychometric Alignment: Capturing Human Knowledge Distributions via Language Models

Reference 74

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Observation f9bdbce1-5d1f-48de-b64e-41ffec152fc4 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Measuring Massive Multitask Language Understanding

Reference 75

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Observation 00cb816c-f9e6-4679-97aa-a3f85c5ec5e7 · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Measuring Mathematical Problem Solving With the MATH Dataset

Reference 76

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Observation ac913df3-750e-488d-8c79-9783db39da81 · outbound

This paper cites Training Compute-Optimal Large Language Models.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Training Compute-Optimal Large Language Models

Reference 77

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Observation 8af74132-cdb8-44f8-bed3-d9a6c34f9230 · outbound

This paper cites The Curious Case of Neural Text Degeneration.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models The Curious Case of Neural Text Degeneration

Reference 78

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Observation 0da5afc7-794f-4b0d-a6e5-2ac3ddc5ab34 · outbound

This paper cites Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor

Reference 79

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Observation 3c6e9091-4c9f-4398-a386-0a006ec3390b · outbound

This paper cites Large Language Models Can Self-Improve.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Large Language Models Can Self-Improve

Reference 80

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Observation a3694079-8ba1-4f51-a90a-282186aa9d14 · outbound

This paper cites Open-endedness is essential for artificial superhuman intelligence, 2024.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Open-endedness is essential for artificial superhuman intelligence, 2024

Reference 81

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Observation 3a061c03-6bfc-41c3-af7c-1ccd99501f48 · outbound

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Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Phi-2: The surprising power of small language models

Reference 82

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Observation ab76df70-6753-472e-b41b-b8d27678b8ee · outbound

This paper cites General intelligence requires rethinking exploration.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models General intelligence requires rethinking exploration

Reference 83

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Observation 06ad8221-2593-467d-b048-7d462f818a90 · outbound

This paper cites Self-planning code generation with large language models, 2024 a.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Self-planning code generation with large language models, 2024 a

Reference 84

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Observation bdb7789a-0e56-494b-8906-9c4d1b8c0e4b · outbound

This paper cites Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws

Reference 85

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Observation 5cbbc3c9-49e4-4da0-b441-d37f4a99ddfc · outbound

This paper cites Mission: Impossible Language Models.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Mission: Impossible Language Models

Reference 86

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Observation d1046448-6e8e-4603-9698-51141f95787a · outbound

This paper cites Scaling Laws for Neural Language Models.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Scaling Laws for Neural Language Models

Reference 87

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Observation 9943dbdd-8938-4107-a15c-1f13f1750abf · outbound

This paper cites Aligning Large Language Models through Synthetic Feedback.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Aligning Large Language Models through Synthetic Feedback

Reference 88

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Observation 13209e60-0223-48ae-ad90-738155a0883a · outbound

This paper cites Understanding the Effects of RLHF on LLM Generalisation and Diversity.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Understanding the Effects of RLHF on LLM Generalisation and Diversity

Reference 89

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Observation b6138706-4deb-459f-b813-80733664939a · outbound

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Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models RL with KL penalties is better viewed as Bayesian inference

Reference 90

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Observation fe491448-3b42-4fb9-b63b-5e8516dc2f54 · outbound

This paper cites Training Language Models to Self-Correct via Reinforcement Learning.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Training Language Models to Self-Correct via Reinforcement Learning

Reference 91

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Observation c5c86204-982b-4715-b3b6-82f55f993042 · outbound

This paper cites Learning to Reason and Memorize with Self-Notes.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Learning to Reason and Memorize with Self-Notes

Reference 92

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Observation 4f05602f-0752-4c6a-a7a7-bffad41cd4b2 · outbound

This paper cites CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning

Reference 93

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Observation 9324db8a-0105-409a-8a16-3a3cfe74a07c · outbound

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Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Beyond Scale: The Diversity Coefficient as a Data Quality Metric for Variability in Natural Language Data

Reference 94

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Observation 941fbff9-7757-4086-94bf-39c697544f26 · outbound

This paper cites Instruction Tuning with Human Curriculum.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Instruction Tuning with Human Curriculum

Reference 95

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Observation 6ed99d71-dfed-40e1-ab46-c691969ea203 · outbound

This paper cites Deduplicating training data makes language models better.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Deduplicating training data makes language models better

Reference 96

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Observation f0f94df1-0c3c-4834-a046-b0c3ed2233a1 · outbound

This paper cites Abandoning objectives: Evolution through the search for novelty alone.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Abandoning objectives: Evolution through the search for novelty alone

Reference 97

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Observation 7d0b9bca-0335-45c3-926b-96272425247b · outbound

This paper cites Evolving a diversity of virtual creatures through novelty search and local competition.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Evolving a diversity of virtual creatures through novelty search and local competition

Reference 98

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Observation e18d0083-7a52-431a-86db-85fefda34a6e · outbound

This paper cites Evolution through Large Models.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Evolution through Large Models

Reference 99

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Observation e37ea368-107c-4581-8034-d9ed45bf1621 · outbound

This paper cites Common 7B Language Models Already Possess Strong Math Capabilities.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Common 7B Language Models Already Possess Strong Math Capabilities

Reference 100

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Observation 97c5a342-eece-47d4-b4d3-bf5b7bead9e8 · outbound

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Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models DataComp-LM: In search of the next generation of training sets for language models

Reference 101

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Pith citing papers

Observation be90d5ce-b715-4011-b0c8-7bc675552597 · inbound

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data cites this paper.

The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-08T04:52:23.165807Z

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Observation 85a4a3b0-7a68-43a8-9211-8b540f8af01b · inbound

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Even Small Reasoners Should Quote Their Sources: Introducing the Pleias-RAG Model Family Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T10:25:14.843945Z

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Observation 60f67684-1c2c-44ac-95b8-4474dc49c70d · inbound

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Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-18T22:46:53.810511Z

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Observation 42cfd222-065e-48ae-a358-ca2839290159 · inbound

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Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T14:13:47.911651Z

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Observation cc68e87c-10ea-400f-bafe-b75d7918390c · inbound

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A Structured Review of Underwater Object Detection Challenges and Solutions: From Traditional to Large Vision Language Models Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models

Reference 157

Resolution: unresolved; no resolver link, observed 2026-08-15T16:09:27.538964Z

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Observation c054272a-e05c-49d4-b976-d52fef094b1e · inbound

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Less is Enough: Synthesizing Diverse Data in LLM Feature Space with Sparse Autoencoders Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T01:17:11.716401Z

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Observation 6183855b-a281-4259-97a5-c825df00506c · inbound

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QDTraj: Exploration of Diverse Trajectory Primitives for Articulated Objects Robotic Manipulation Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-05-08T21:49:16.087338Z

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Observation 445ca751-b242-46fb-bb21-5ef8c3ac78a1 · inbound

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Redefining Maritime Anomaly Detection via Equation-Grounded Synthetic Anomalies Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-06-30T07:54:21.928310Z

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