Paper Citation Record · LEDGER

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators

As of 15 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2607.20465.

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

pith.paper-citation-record.v1
2607.20465 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T13:43:29.483303Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

46 of 46 outbound references displayed

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verified fuzzy0
unresolved46
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External citation measurements

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Outbound references

Observation bd4b03ec-4b6f-40c5-8b3e-b5487fe65a13 · outbound

This paper cites Fowlkes, Stefano Soatto, and Pietro Perona.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Fowlkes, Stefano Soatto, and Pietro Perona

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.268938Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cdffe553-588b-46b7-888b-cadc8844152d · outbound

This paper cites A Survey of Multimodal Large Language Model from A Data-centric Perspective.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators A Survey of Multimodal Large Language Model from A Data-centric Perspective

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.274883Z

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Observation fd684c46-25f3-4722-bfef-fec878732057 · outbound

This paper cites Text2sql-flow: A robust sql-aware data augmentation framework for text-to-sql.arXiv preprint arXiv:2511.10192, 2025.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Text2sql-flow: A robust sql-aware data augmentation framework for text-to-sql.arXiv preprint arXiv:2511.10192, 2025

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.281395Z

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Unavailable: canonical work link unavailable.

Observation a93b4c45-4b63-45cb-be31-147ea1e05d4b · outbound

This paper cites Data-juicer: A one-stop data processing system for large language models.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Data-juicer: A one-stop data processing system for large language models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.286818Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcbe75e0-c1c3-41c6-a1a3-81dff4e03df5 · outbound

This paper cites Dc-bench: Dataset condensation benchmark.Advances in Neural Information Processing Systems, 35:810–822, 2022.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Dc-bench: Dataset condensation benchmark.Advances in Neural Information Processing Systems, 35:810–822, 2022

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.292145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c4aa522-9ce7-4455-859b-e95d8a464882 · outbound

This paper cites Enhancing chat language models by scaling high-quality instructional conversations, 2023.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Enhancing chat language models by scaling high-quality instructional conversations, 2023

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.296952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 603995aa-e9eb-4334-a2a1-06b30605e8f0 · outbound

This paper cites Unleashing LLM Reasoning Capability via Scalable Question Synthesis from Scratch.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Unleashing LLM Reasoning Capability via Scalable Question Synthesis from Scratch

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.303491Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a22e3b31-e2d7-4070-a10c-45ae2789e857 · outbound

This paper cites MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.308285Z

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Unavailable: canonical work link unavailable.

Observation 941e3dc9-bf3c-4716-a14f-fbaec6ad7448 · outbound

This paper cites The Vendi Score: A Diversity Evaluation Metric for Machine Learning.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators The Vendi Score: A Diversity Evaluation Metric for Machine Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.312742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 858d25fd-5f67-4e8b-aa18-786e218ef8c7 · outbound

This paper cites Datacomp: In search of the next generation of multimodal datasets.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Datacomp: In search of the next generation of multimodal datasets

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.317738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96272a94-f334-4826-be5b-c904b8f16b8c · outbound

This paper cites Closing the data loop: Using opendataarena to engineer superior training datasets.arXiv preprint arXiv:2601.09733, 2025.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Closing the data loop: Using opendataarena to engineer superior training datasets.arXiv preprint arXiv:2601.09733, 2025

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.321741Z

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Observation edec8fc0-5fe7-4784-8327-c3dd57a4efee · outbound

This paper cites The Llama 3 Herd of Models.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators The Llama 3 Herd of Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.326449Z

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Observation 8260ba64-d930-43a0-9770-7534233be136 · outbound

This paper cites Textbooks Are All You Need.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Textbooks Are All You Need

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.331049Z

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Observation fd6371c9-9507-4444-b855-2c9090b67b95 · outbound

This paper cites Lawyer llama technical report, 2023.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Lawyer llama technical report, 2023

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.335854Z

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Observation 9c181c15-a749-47ab-9e2a-19cf6cb0d272 · outbound

This paper cites Mistral 7B.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Mistral 7B

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.340436Z

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Observation 56135507-05ce-4684-a697-169e33636bf7 · outbound

This paper cites Scaling Laws for Neural Language Models.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Scaling Laws for Neural Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.344746Z

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Observation cc07498d-6c21-4743-bacd-6e6d90f45803 · outbound

This paper cites Miranda, Alisa Liu, Nouha Dziri, Shane Lyu, Yuling Gu, Saumya Malik, Victoria Graf, Jena D.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Miranda, Alisa Liu, Nouha Dziri, Shane Lyu, Yuling Gu, Saumya Malik, Victoria Graf, Jena D

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.349168Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 798812e9-500e-42ac-beab-8982f38a73f6 · outbound

This paper cites Datacomp-lm: In search of the next generation of training sets for language models.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Datacomp-lm: In search of the next generation of training sets for language models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.353546Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29130112-8ba6-494e-9325-3879df750380 · outbound

This paper cites Infinity Instruct: Scaling Instruction Selection and Synthesis to Enhance Language Models.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Infinity Instruct: Scaling Instruction Selection and Synthesis to Enhance Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.358479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32307cbf-ed4d-4525-ba4b-b64736018cbd · outbound

This paper cites Superfiltering: Weak-to-strong data filtering for fast instruction-tuning.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Superfiltering: Weak-to-strong data filtering for fast instruction-tuning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.363120Z

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Unavailable: canonical work link unavailable.

Observation 951c07c5-babc-41dd-9ebd-a3df9f7e8723 · outbound

This paper cites Dataflow: An llm-driven framework for unified data preparation and workflow automation in the era of data-centric ai.arXiv preprint arXiv:2512.16676, 2025.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Dataflow: An llm-driven framework for unified data preparation and workflow automation in the era of data-centric ai.arXiv preprint arXiv:2512.16676, 2025

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.368048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0018cca2-de3d-4cf0-89a8-2fcedc1c0926 · outbound

This paper cites Data preparation for large language models.Journal of Computer Science and Technology, 2026.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Data preparation for large language models.Journal of Computer Science and Technology, 2026

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.372838Z

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Unavailable: canonical work link unavailable.

Observation b170a5ed-a4c8-4819-8681-4183ffe4a4bf · outbound

This paper cites What makes good data for alignment? a comprehensive study of automatic data selection in instruction tuning.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators What makes good data for alignment? a comprehensive study of automatic data selection in instruction tuning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.377408Z

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Observation 8e194211-7910-45fc-ab33-883e69dbf12e · outbound

This paper cites Dataperf: Benchmarks for data-centric ai development.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Dataperf: Benchmarks for data-centric ai development

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.382203Z

Source-reported events for the cited work

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Observation e5fcee22-949f-4808-97ea-f56f89739ed4 · outbound

This paper cites GPT-4 technical report, 2023.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators GPT-4 technical report, 2023

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.386982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1908213-b27b-41dd-8382-606338e87975 · outbound

This paper cites Towards tailored recovery of lexical diversity in literary machine translation.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Towards tailored recovery of lexical diversity in literary machine translation

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.391092Z

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Observation 0428d130-e587-457f-8628-4a16b3509635 · outbound

This paper cites Fino1: On the Transferability of Reasoning-Enhanced LLMs and Reinforcement Learning to Finance.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Fino1: On the Transferability of Reasoning-Enhanced LLMs and Reinforcement Learning to Finance

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.395146Z

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Unavailable: canonical work link unavailable.

Observation 3ccdba98-9f92-4550-a0c8-458feef7a0ab · outbound

This paper cites A survey on domain adaptation theory: learning bounds and theoretical guarantees.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators A survey on domain adaptation theory: learning bounds and theoretical guarantees

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.400467Z

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Observation ae485336-5f26-4516-87d6-4d769b4338dc · outbound

This paper cites Let’s verify math questions step by step.arXiv preprint arXiv:2505.13903, 2025.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Let’s verify math questions step by step.arXiv preprint arXiv:2505.13903, 2025

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.404990Z

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Unavailable: canonical work link unavailable.

Observation 5b144c66-67dc-4ca7-8326-4f5ae8b151e7 · outbound

This paper cites Reasonmed: A 370k multi-agent generated dataset for advancing medical reasoning,.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Reasonmed: A 370k multi-agent generated dataset for advancing medical reasoning,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.409080Z

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Observation 7f9efaef-e9bc-4663-b11d-f628591b0d7d · outbound

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

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators LLaMA: Open and Efficient Foundation Language Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.418322Z

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Unavailable: canonical work link unavailable.

Observation e9eb37d7-e4c7-44c3-8ed3-777f9175fe87 · outbound

This paper cites Self-instruct: Aligning language models with self-generated instructions.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Self-instruct: Aligning language models with self-generated instructions

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.422758Z

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Observation 8a4bba88-7a4f-4b5c-b9e0-987ea6a9ec2b · outbound

This paper cites QuRating: Selecting High-Quality Data for Training Language Models.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators QuRating: Selecting High-Quality Data for Training Language Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.427294Z

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Observation d3d50ac5-3475-4a71-a7fc-109dec7d815d · outbound

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

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators LESS: Selecting Influential Data for Targeted Instruction Tuning

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.432403Z

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Observation 31dd247f-db92-44bf-b5b7-d0f7224e1082 · outbound

This paper cites Wizardlm: Empowering large pre-trained language models to follow complex instructions, 2023.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Wizardlm: Empowering large pre-trained language models to follow complex instructions, 2023

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.438152Z

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Observation cd37b376-0ddf-4eb3-9bed-1cd38b27c9e0 · outbound

This paper cites Logics-stem: Empowering llm reasoning via failure-driven post-training and document knowledge enhancement, 2026.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Logics-stem: Empowering llm reasoning via failure-driven post-training and document knowledge enhancement, 2026

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.443264Z

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Observation ab7bbbe0-d135-48ed-9c48-1b67fee50a50 · outbound

This paper cites Qwen2 Technical Report.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Qwen2 Technical Report

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.447430Z

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Observation 2c5787fa-93fb-43fc-811f-d053fa1ca382 · outbound

This paper cites Qwen2.5 Technical Report.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Qwen2.5 Technical Report

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.452083Z

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Observation a1be480d-9b52-4913-8e55-650ef7ec0d5b · outbound

This paper cites OpenCSG Chinese Corpus: A Series of High-quality Chinese Datasets for LLM Training.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators OpenCSG Chinese Corpus: A Series of High-quality Chinese Datasets for LLM Training

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.456364Z

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Observation 2a79cf5e-56d5-45fa-b806-dfec79adf9fb · outbound

This paper cites Disc-lawllm: Fine-tuning large language models for intelligent legal services, 2023.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Disc-lawllm: Fine-tuning large language models for intelligent legal services, 2023

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.460718Z

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Observation ca1f35df-7d2d-4df3-852a-e25dd474a82b · outbound

This paper cites Ultramedical: Building specialized generalists in biomedicine, 2024.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Ultramedical: Building specialized generalists in biomedicine, 2024

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.464976Z

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Observation 8f808fa4-a4bc-489c-80c0-886038268843 · outbound

This paper cites LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.469176Z

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Observation 4720352e-9d9c-4b37-ac62-f800bdecbbd1 · outbound

This paper cites Google-proof.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Google-proof

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.473875Z

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Observation 27cc783e-2d4e-47a6-837b-b1d93658916a · outbound

This paper cites an unresolved cited work.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Unresolved cited work

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.478789Z

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Observation a483e706-d926-47cf-b11a-b27d902d965a · outbound

This paper cites an unresolved cited work.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Unresolved cited work

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.483303Z

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Observation 17223647-62dc-4e87-b1fc-eedfb60ea8fc · outbound

This paper cites an unresolved cited work.

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators Unresolved cited work

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:29.413829Z

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Pith citing papers

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