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Paper Citation Record · LEDGER

Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs

As of 7 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 1 inbound Pith citation observation for arXiv:2507.18055.

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

pith.paper-citation-record.v1
2507.18055 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:43:30.905284Z

measured 20 of 20 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T23:43:05.423799Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

19 of 19 outbound references displayed

  • verified exact0
  • verified fuzzy12
  • unresolved7
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 07fe07c5-6d8d-4ad3-9ed7-9a82238dc1dd · outbound

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

Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs Generative AI for Synthetic Data Generation: Methods, Challenges and the Future

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-06T14:43:28.885311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:43:28.885311Z digest=sha256:f86f3d4852f7a0fbbfb9a2da065d823102fdf98b36975452ed75b65422140bea

Observation c1f52099-b92f-47b6-9d07-143fdc29dfaa · outbound

This paper cites Synthetic Data in AI: Challenges, Applications, and Ethical Implications.

Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs Synthetic Data in AI: Challenges, Applications, and Ethical Implications

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-06T14:43:28.979022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:43:28.979022Z digest=sha256:03b9e817d397c7dc558daad24485aeb189abcb6de84aa9831146fb81ad8028e8

Observation 3bf9f5f8-1532-458e-b6cd-e8e137588b1a · outbound

This paper cites Extracting training data from large language models.

Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs Extracting training data from large language models

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-06T14:43:29.064360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:43:29.064360Z digest=sha256:f8d7161a5f365b4ca1d173371eef4cf594e0cf8d9ac91bffab80bac36bccf9ff

Observation c3da9d12-ace7-4510-a590-9919dcbd39d7 · outbound

This paper cites Security and privacy challenges of large language models: A survey.ACM Computing Surveys, 57(6):1–39, 2025.

Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs Security and privacy challenges of large language models: A survey.ACM Computing Surveys, 57(6):1–39, 2025

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-06T14:43:29.202971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:43:29.202971Z digest=sha256:d6bfa9c003f1808db972c1b7e485626fe5e0c5cecae5234882778e3e6878c20a

Observation 2820e3f1-a4ea-489b-bc0f-86783df570f0 · outbound

This paper cites Evaluating large language models in generating synthetic hci research data: a case study.

Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs Evaluating large language models in generating synthetic hci research data: a case study

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:43:33.656799Z

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.

source=pdf_text observed=2026-08-06T14:43:29.305712Z digest=sha256:5b2e597d3d5d7025be3d30acac056595e1383b42ebe2578b917c092acb1d32a1

Observation e7cc00ab-b9ee-46c9-a5fb-52b4d7868f32 · outbound

This paper cites an unresolved cited work.

Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs Unresolved cited work

Reference 6

Resolution
unresolved
raw_fallback, observed 2026-08-06T14:43:33.445971Z

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.

source=pdf_text observed=2026-08-06T14:43:29.412622Z digest=sha256:903738c20685f197929a1bed83924c5b36bb44ba21fedbbb6ec9a467b96b061f

Observation e8dc92ce-77ef-46a8-934a-37309d503710 · outbound

This paper cites Siu, Byron C.

Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs Siu, Byron C

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:43:33.288280Z

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.

source=pdf_text observed=2026-08-06T14:43:29.546725Z digest=sha256:95e20bdba07b6b80eee0a1530a44ce465ed7b2696111bc108b25ba7d5466849e

Observation b54b1180-67fc-43aa-8a3a-b9da7ac3e24a · outbound

This paper cites Neural review rating prediction with user and product memory.

Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs Neural review rating prediction with user and product memory

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:43:33.111569Z

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.

source=pdf_text observed=2026-08-06T14:43:29.653053Z digest=sha256:186c0e1f89297638a5ac62c03d066b00efb4dfc4258b36acc83f607fb0fe36ad

Observation b18bae4f-53a2-4eb1-b9f7-0bac598d4124 · outbound

This paper cites Jointly measuring diversity and quality in text generation models, 2019.

Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs Jointly measuring diversity and quality in text generation models, 2019

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-06T14:43:29.795320Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:43:29.795320Z digest=sha256:e4fc9e6139511a47f7eafd9eb44078696f63fb0e8c0e69366c930b55919a5f5c

Observation 9fcf71af-0a5c-4d54-b847-7aa98ca8560c · outbound

This paper cites Lautrup, Tobias Hyrup, Arthur Zimek, and Peter Schneider-Kamp.

Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs Lautrup, Tobias Hyrup, Arthur Zimek, and Peter Schneider-Kamp

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:43:32.948413Z

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.

source=pdf_text observed=2026-08-06T14:43:29.926384Z digest=sha256:ca26cfbdd3d70b74ef5d76295888eb7165ea8aee6e8db327f1506c66817bbae9

Observation 4a14cdd5-e996-4f5d-bf35-f7c077786f05 · outbound

This paper cites Della Vedova, Daniele Tessera, Daniele Toti, and Nicola Vanoli.

Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs Della Vedova, Daniele Tessera, Daniele Toti, and Nicola Vanoli

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:43:32.761295Z

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.

source=pdf_text observed=2026-08-06T14:43:30.054165Z digest=sha256:bd6e781b2a25b13a5497193b6b8133ff37c068792ada734d25764e50f5d80bf7

Observation 7f42d7f7-c46a-4d6f-8511-b603a94d9871 · outbound

This paper cites Real risks of fake data: Synthetic data, diversity-washing and consent circumvention.

Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs Real risks of fake data: Synthetic data, diversity-washing and consent circumvention

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:43:32.559264Z

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.

source=pdf_text observed=2026-08-06T14:43:30.143810Z digest=sha256:05abdc47e7d01b1042e37758d721e4eb40c4cfe33bb9a1439d6debcd83821e02

Observation a11426cc-f9d7-41d9-8d96-93e79af1b396 · outbound

This paper cites A comprehensive review of current trends, challenges, and opportunities in text data privacy.Computers & Security, 151:104358, 2025.

Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs A comprehensive review of current trends, challenges, and opportunities in text data privacy.Computers & Security, 151:104358, 2025

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:43:32.354203Z

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.

source=pdf_text observed=2026-08-06T14:43:30.265955Z digest=sha256:a24f296fe99277560f715abb62732b085ee3a5a4e2b07e5a3738ee3d974c46a6

Observation 4721f788-142f-474b-90e6-cf5c614cc32a · outbound

This paper cites an unresolved cited work.

Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs Unresolved cited work

Reference 14

Resolution
unresolved
raw_fallback, observed 2026-08-06T14:43:32.148452Z

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.

source=pdf_text observed=2026-08-06T14:43:30.380157Z digest=sha256:f8eac796f75f803ed781ee255eec210118e1444a8c2126d3a1a18061c5e05d3b

Observation 13720b7a-c11c-4037-9d57-cde4e54bae5c · outbound

This paper cites Flair: An easy-to-use framework for state-of-the-art nlp.

Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs Flair: An easy-to-use framework for state-of-the-art nlp

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:43:31.968489Z

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.

source=pdf_text observed=2026-08-06T14:43:30.483301Z digest=sha256:a148e9ec0d8b4fa7e3079af28999b52ea01a7e56e08eafda8e0ae3308df534cb

Observation acb65443-d06e-4cbe-a1c8-6e72b709789f · outbound

This paper cites the", "is.

Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs the", "is

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:43:31.776473Z

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.

source=pdf_text observed=2026-08-06T14:43:30.590011Z digest=sha256:a06792f1e68c5794b75b77cd745c3d8e935e52625618121188472e33c16e6642

Observation 2737e222-924a-48e7-bff0-95f1fd34324b · outbound

This paper cites Fell apart after a couple washes.

Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs Fell apart after a couple washes

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:43:31.569623Z

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.

source=pdf_text observed=2026-08-06T14:43:30.702813Z digest=sha256:90bb725db250f2c0a3a0e98d0c6ec115e4f4008a833e5d5d238e098be59736ba

Observation 7f50b40c-401a-451e-8874-c05145b2c98f · outbound

This paper cites my daughter,.

Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs my daughter,

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:43:31.353849Z

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.

source=pdf_text observed=2026-08-06T14:43:30.784214Z digest=sha256:0ba4e1a3eff3a77162a4bde37f1eadcbdbd13077d95da1ead492fb466857dd5c

Observation 6759d421-0a3f-42a2-aaa1-81c9f1108541 · outbound

This paper cites Figure 6 shows this distribution, where each value reflects how similar a user’s writing style is to the rest of the population.

Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs Figure 6 shows this distribution, where each value reflects how similar a user’s writing style is to the rest of the population

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T14:43:31.132461Z

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.

source=pdf_text observed=2026-08-06T14:43:30.905284Z digest=sha256:f11fd0c0950c9dec81c65dc1b6068a9364ede4e6ba63b4b394c82b3b27b7d085

Pith citing papers

Observation 33449d48-ee6b-4199-b1c5-e86cb48708a2 · inbound

Partition, Prompt, Aggregate: Statistical Self-Consistency in Language Models cites this paper.

Partition, Prompt, Aggregate: Statistical Self-Consistency in Language Models Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-01T23:43:05.423799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T23:43:05.423799Z digest=sha256:402e2fbc7a72f81a86593a5eea6bf5b9e87de86c2f172b1fdb9862c224998a9b