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Paper Citation Record · LEDGER

On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 38 inbound Pith citation observations for arXiv:2406.15126.

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

pith.paper-citation-record.v1
2406.15126 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 38 of 38 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T19:38:41.472257Z

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

0 of 0 outbound references displayed

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External citation measurements

6
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 890944fb-0147-4a33-afbe-c6476202fa84 · inbound

ShieldGemma: Generative AI Content Moderation Based on Gemma cites this paper.

ShieldGemma: Generative AI Content Moderation Based on Gemma On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 15

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arxiv_id, observed 2026-05-20T13:17:39.527398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-20T13:17:39.444002Z digest=sha256:24d5caa91f40385e344685ce01a78c1dc07773e6c562ad9d8dde0faedae1fe73

Observation f90a9726-9b3e-40f9-bb86-91ce44c8e866 · inbound

Understanding the Effectiveness of LLMs in Automated Self-Admitted Technical Debt Repayment cites this paper.

Understanding the Effectiveness of LLMs in Automated Self-Admitted Technical Debt Repayment On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 19

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source=pdf_text observed=2026-08-10T19:38:41.472257Z digest=sha256:5f3e44ddf7717a93415fd5a05e6698755c3ea80bf5153426e548721f512af51c

Observation 4defbc41-2b4f-443d-b9d7-b2b42e665400 · inbound

OceanChat: The Effect of Virtual Conversational AI Agents on Sustainable Attitude and Behavior Change cites this paper.

OceanChat: The Effect of Virtual Conversational AI Agents on Sustainable Attitude and Behavior Change On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 31

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source=pdf_text observed=2026-08-09T10:52:46.505878Z digest=sha256:5972549a7d3504317a25cb6fc8ab5f1ea186c32f2ea51575371fbfd5825867e0

Observation 23228c18-f2e8-4730-97fe-aa44f885fcff · inbound

LLMs can be easily Confused by Instructional Distractions cites this paper.

LLMs can be easily Confused by Instructional Distractions On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 20

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no resolver link, observed 2026-08-09T10:50:12.345590Z

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source=arxiv_source observed=2026-08-09T10:50:12.345590Z digest=sha256:1f1fcf4f9f883d87bc5a1fb011cace5fad3a92cd7ac6b1cce925856de86dc5f3

Observation e238ed7d-d92f-4022-b544-da65095d585e · inbound

Salamandra Technical Report cites this paper.

Salamandra Technical Report On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 123

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T04:58:33.127252Z digest=sha256:acbc8cbc4f72ad01f65759a1c77dc0109dd6af50517199ff5ee11fc150b3b98a

Observation 47a8d6a7-e4ec-4d07-b961-9eb6cd52bc93 · inbound

Measuring Diversity in Synthetic Datasets cites this paper.

Measuring Diversity in Synthetic Datasets On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 48

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source=arxiv_source observed=2026-08-08T04:54:50.783032Z digest=sha256:975007f14f1056ce7f9d3557c38967773d04a5310f948bf4ac980339cd809aac

Observation f6c3afb6-5661-4ef6-9c40-b6966b13cfa6 · inbound

Few-shot LLM Synthetic Data with Distribution Matching cites this paper.

Few-shot LLM Synthetic Data with Distribution Matching On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 29

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no resolver link, observed 2026-08-08T17:20:36.520045Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T17:20:36.520045Z digest=sha256:de7af7f42a8c9287ccd12a2956e6e27e0fe88b971984e104fd90b1c65b09279e

Observation 83cfefb7-5384-4832-8fec-07716a0c6b44 · inbound

Sustainability via LLM Right-sizing cites this paper.

Sustainability via LLM Right-sizing On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 22

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arxiv_id, observed 2026-05-22T19:25:03.702637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-22T19:24:58.690462Z digest=sha256:f41495dbacd9cedbf9fb8daa94e2b0cb205f7e725a753c2df09087896d7f5b7c

Observation 4c48cbe3-b118-422a-b45b-530734da7e96 · inbound

TF1-EN-3M: Three Million Synthetic Moral Fables for Training Small, Open Language Models cites this paper.

TF1-EN-3M: Three Million Synthetic Moral Fables for Training Small, Open Language Models On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 3

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arxiv_id, observed 2026-05-22T19:01:57.855097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-22T19:01:42.307514Z digest=sha256:799c7f768d21314e30c6e367fce8d526f408fc7d584c65fd6920a5150a0b5bba

Observation 725c5199-8b6e-437d-b9e0-eb3305b62565 · inbound

JARVIS: A Multi-Agent Code Assistant for High-Quality EDA Script Generation cites this paper.

JARVIS: A Multi-Agent Code Assistant for High-Quality EDA Script Generation On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 16

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:29:58.568816Z digest=sha256:d2a4ce9240b67fdae52254b3b38bab381fe7a628b6cfcf06d4df9c89227ea213

Observation f413e2ad-43c6-4d89-a4db-76e344c06d18 · inbound

Large language model as user daily behavior data generator: balancing population diversity and individual personality cites this paper.

Large language model as user daily behavior data generator: balancing population diversity and individual personality On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 18

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no resolver link, observed 2026-08-07T14:49:32.064271Z

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source=arxiv_source observed=2026-08-07T14:49:32.064271Z digest=sha256:0b90ab5a33d0df25fbdabb408af1ef79b334e71a5d26aaeba49aaf32841f87dd

Observation a6823ea5-7bdf-4c84-a979-7dd7cfc98994 · inbound

A Survey of LLM $\times$ DATA cites this paper.

A Survey of LLM $\times$ DATA On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 272

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no resolver link, observed 2026-08-07T14:33:13.460742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:33:13.460742Z digest=sha256:28e1a6f37df1e38403da92fff7512017112f01e6b603966acdb221144736270e

Observation e988690b-12a9-44ef-9570-b074cb7ce78d · inbound

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

GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 22

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no resolver link, observed 2026-08-07T14:00:31.486151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:00:31.486151Z digest=sha256:99756a4a43714950eb828808a9d2d0e9d42642210586fec421ea1196a8e079a8

Observation 99359307-681a-4093-852c-b3bfbc5a5ed4 · inbound

Beyond Keywords: Evaluating Large Language Model Classification of Nuanced Ableism cites this paper.

Beyond Keywords: Evaluating Large Language Model Classification of Nuanced Ableism On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 22

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no resolver link, observed 2026-08-07T13:56:04.125085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:56:04.125085Z digest=sha256:9b6060ee87e9570f1dffc546653eb0d727b1a48f9bf6c888e5cfab6faa4b8011

Observation bd5b2807-5380-4afa-b7fd-cb19bde98ccd · inbound

Self-Error-Instruct: Generalizing from Errors for LLMs Mathematical Reasoning cites this paper.

Self-Error-Instruct: Generalizing from Errors for LLMs Mathematical Reasoning On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 10

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source=pdf_text observed=2026-08-07T13:08:01.650048Z digest=sha256:676f81301679cbd0ffb4cb9b5f736feaa5c6617567684a7c981d296a194e9122

Observation b8060b6a-96bb-4117-b0b6-c8d9a0695d7d · inbound

Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for LLMs cites this paper.

Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for LLMs On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 51

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no resolver link, observed 2026-08-07T12:44:06.650241Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:44:06.650241Z digest=sha256:80e77756311663a99fb48771932610751adb0e25ee20810d058c1bdf950d5aeb

Observation 3c26e6a5-b823-4894-9ec8-1e16ed46b9f6 · inbound

Mind the Quote: Enabling Quotation-Aware Dialogue in LLMs via Plug-and-Play Modules cites this paper.

Mind the Quote: Enabling Quotation-Aware Dialogue in LLMs via Plug-and-Play Modules On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 11

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source=pdf_text observed=2026-08-07T12:35:30.421658Z digest=sha256:d4ddfe15a0b394052bbcc8518f5d5d2f20a3c0ffca2679b87c631727236add3b

Observation 2696c1ba-bdf1-49a3-b809-0e61438c4ba9 · inbound

RefEdit: A Benchmark and Method for Improving Instruction-based Image Editing Model on Referring Expressions cites this paper.

RefEdit: A Benchmark and Method for Improving Instruction-based Image Editing Model on Referring Expressions On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 24

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source=pdf_text observed=2026-08-07T11:07:56.669407Z digest=sha256:dee3266d2469bde015b09395ad7cc69681311ca5ff1b0657138de81f6e1b1b8e

Observation bab40330-8c93-4715-950b-a85c840ea0ef · inbound

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

Does Prompt Design Impact Quality of Data Imputation by LLMs? On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 21

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source=arxiv_source observed=2026-08-07T10:50:50.163370Z digest=sha256:5cda8b6ef43f09f6d7f078ce1187ba41c7cf623f32e74a7deb7c38ea7b8b26d3

Observation 93bbf116-0989-4c37-8b3c-b4001d090f20 · inbound

Foundation Model Empowered Synesthesia of Machines (SoM): AI-native Intelligent Multi-Modal Sensing-Communication Integration cites this paper.

Foundation Model Empowered Synesthesia of Machines (SoM): AI-native Intelligent Multi-Modal Sensing-Communication Integration On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 21

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source=pdf_text observed=2026-08-07T05:33:50.951802Z digest=sha256:a80dec4d69b05a485a26b12253558bdf58815af080a09c7be7e911a40a3a1fa9

Observation 574188a8-7994-4b1b-aa44-c4fafaf13b44 · inbound

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

Unlocking the Potential of Large Language Models in the Nuclear Industry with Synthetic Data On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 8

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no resolver link, observed 2026-08-07T05:05:37.118238Z

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source=pdf_text observed=2026-08-07T05:05:37.118238Z digest=sha256:e48eade9c1edc15e8388913864ee03fe7e5ca3e42d172ad42a04a78e9dfed0f2

Observation fcd82b9e-de32-48a2-8c7e-f66f1cbf1563 · inbound

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

MDBench: A Synthetic Multi-Document Reasoning Benchmark Generated with Knowledge Guidance On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 13

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source=arxiv_source observed=2026-08-07T00:14:06.967525Z digest=sha256:18670423c98d8352abdf60c37a4576fcc0195d071246d207ec3f18b3e7ee1e29

Observation c3dea55e-8b96-4cb9-97c7-39ffa85710e5 · inbound

Enterprise Large Language Model Evaluation Benchmark cites this paper.

Enterprise Large Language Model Evaluation Benchmark On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 30

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no resolver link, observed 2026-08-06T22:56:32.834913Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:56:32.834913Z digest=sha256:761de19f416c939b17c997778dbc15283fcbfa1683e1c35e36b68a23de0cf8b9

Observation 1b340424-07fc-4105-aed3-5e3db21e1b0c · inbound

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead cites this paper.

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 235

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source=pdf_text observed=2026-08-06T21:36:40.094301Z digest=sha256:981b7a7e32a085c94b6ec137cb20f16686efeaebdbc3bc8fe0fdd920d5029bb9

Observation 7e4b5e67-e0bb-4d3f-bf5f-471fee0c1b7d · inbound

Multimodal Mathematical Reasoning with Diverse Solving Perspective cites this paper.

Multimodal Mathematical Reasoning with Diverse Solving Perspective On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 74

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source=pdf_text observed=2026-08-06T20:25:24.594546Z digest=sha256:d8fc73923db7f5673247743abd4a9be27d115dae9a1b9710819c396ec768cd64

Observation f3f078ea-e490-4762-8c9a-1bafb49c87b4 · inbound

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

A Penalty Goes a Long Way: Measuring Lexical Diversity in Synthetic Texts Under Prompt-Influenced Length Variations On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 23

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no resolver link, observed 2026-08-06T15:46:27.789467Z

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source=arxiv_source observed=2026-08-06T15:46:27.789467Z digest=sha256:adfe9a9721bb9c046f07a32e4cc97486352219503d9224a11b3468d28764b9eb

Observation 2e77c0ea-1796-4f21-a6a5-65b116f4353f · inbound

StaAgent: An Agentic Framework for Testing Static Analyzers cites this paper.

StaAgent: An Agentic Framework for Testing Static Analyzers On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 22

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source=pdf_text observed=2026-08-06T15:50:37.908542Z digest=sha256:494dbc73fff29fa44c6ba4c41b01bf55ff3a88d1d05d417a71b65c490982561c

Observation 2638cc1b-afbf-463a-af2d-1ee6cb7c114c · inbound

Separation Logic of Generic Resources via Sheafeology cites this paper.

Separation Logic of Generic Resources via Sheafeology On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 14

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source=pdf_text observed=2026-08-06T05:22:29.785059Z digest=sha256:7eb1739e209aea8363965754d00d19ac4b25ff2587280be76ed9392d559a7901

Observation 712f1cf3-7c7d-49bc-8a5e-da308e5f1203 · inbound

Can Structured Templates Facilitate LLMs in Tackling Harder Tasks? : An Exploration of Scaling Laws by Difficulty cites this paper.

Can Structured Templates Facilitate LLMs in Tackling Harder Tasks? : An Exploration of Scaling Laws by Difficulty On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 17

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source=pdf_text observed=2026-08-05T16:02:03.543010Z digest=sha256:c1894b5b389a82f2ad608a17a68245f343151a6c73578edb2109f576db363eda

Observation 20af70e1-bc96-44b8-bc74-c32143b195a6 · inbound

Ensembling LLM-Induced Decision Trees for Explainable and Robust Error Detection cites this paper.

Ensembling LLM-Induced Decision Trees for Explainable and Robust Error Detection On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 23

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no resolver link, observed 2026-08-03T18:04:30.736714Z

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source=pdf_text observed=2026-08-03T18:04:30.736714Z digest=sha256:bc10dac81a8751b6e4e6f9ab69164a4514f2fa5179fe3824b99b50aa6fe8d261

Observation d3266ee6-522b-4bee-9f24-2c6dec1c55d6 · 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 On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 14

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no resolver link, observed 2026-08-03T01:17:11.752127Z

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source=pdf_text observed=2026-08-03T01:17:11.752127Z digest=sha256:94355af8f735280228742ad3e7430c819ce5cc05ab65fea78980e6be15ad0a3e

Observation a659fb5e-e911-4f3a-ab29-9e12d0ff0099 · inbound

Evaluating Zero-Shot and One-Shot Adaptation of Small Language Models in Leader-Follower Interaction cites this paper.

Evaluating Zero-Shot and One-Shot Adaptation of Small Language Models in Leader-Follower Interaction On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 29

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no resolver link, observed 2026-08-02T20:26:40.494982Z

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source=pdf_text observed=2026-08-02T20:26:40.494982Z digest=sha256:c03044c528b37f6220ea82a34bb468d90af2d245280f68079b974335480c4c6b

Observation 9c00e8b3-e66b-48be-b0ad-7a4b50ce7297 · inbound

Synthetic Data Generation for Brain-Computer Interfaces: Overview, Benchmarking, and Future Directions cites this paper.

Synthetic Data Generation for Brain-Computer Interfaces: Overview, Benchmarking, and Future Directions On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 119

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metadata mismatch
arxiv_id, observed 2026-05-21T11:30:02.555967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-21T11:26:13.074820Z digest=sha256:6d08fbd5e278f4fcdffc9524c23d7e951142dd9ae2b340198be4b65b14499cc3

Observation 23c81e38-a8d9-4451-b942-6d7d66a9dcde · inbound

STELLAR-E: a Synthetic, Tailored, End-to-end LLM Application Rigorous Evaluator cites this paper.

STELLAR-E: a Synthetic, Tailored, End-to-end LLM Application Rigorous Evaluator On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 19

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arxiv_id, observed 2026-05-11T22:01:10.900240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-08T03:39:30.528601Z digest=sha256:2fbc3aeffe34be33a8ad13298f4d755759bfb53e3b893f3527ad1c3d262a6d45

Observation 504947f0-1718-445b-9340-02f3f479b4cc · inbound

Generalistic or Specific Embeddings, Which is Better? An Empirical Study on Search for Clinical Coding in Non-English Languages cites this paper.

Generalistic or Specific Embeddings, Which is Better? An Empirical Study on Search for Clinical Coding in Non-English Languages On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 29

Resolution
metadata mismatch
arxiv_id, observed 2026-06-29T07:33:14.004650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-29T07:27:03.060416Z digest=sha256:7f22ba9d93a7dea1abfd06c5947826af9a2fba05e915fd2e38a9333222df78b8

Observation b07bf8c5-9b38-4291-b43e-1e213c5f7033 · inbound

EHRBench: An Automated and Reliable EHR-based Benchmark for Clinical Decision Making with LLMs cites this paper.

EHRBench: An Automated and Reliable EHR-based Benchmark for Clinical Decision Making with LLMs On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 62

Resolution
verified exact
arxiv_id, observed 2026-06-29T06:43:10.450895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-29T06:41:06.828814Z digest=sha256:75b7bce110445a6c59be5df235b686b538c7aeaa33d696e74597cb8a55ba5a38

Observation 743079c3-2c4b-4f49-959f-2c9ce247e40d · inbound

Afrispeech Semantics: Evaluating Audio Semantic Reasoning in Spoken Language Models Across Domains and Accents cites this paper.

Afrispeech Semantics: Evaluating Audio Semantic Reasoning in Spoken Language Models Across Domains and Accents On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 183

Resolution
verified exact
arxiv_id, observed 2026-06-30T22:15:05.703956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-06-30T22:11:44.891731Z digest=sha256:a9cfb378ca29c18cbeaea6fae3969ef1dbffd17a95c708d1138e5db69887ffa9

Observation 12b1cfe5-285e-4912-9d0e-03f1aeea657f · inbound

Occupational Prompting Reveals Cultural Bias in Large Language Models cites this paper.

Occupational Prompting Reveals Cultural Bias in Large Language Models On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 39

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T18:04:57.826486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-30T18:03:09.275405Z digest=sha256:3d8a9304a119a3b5be4ba5c5a84999638eddc69c47b7b3aac48b4464ecbabac1