Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications | Paper Citation Record

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This paper cites https://www.theverge.com/2023/12/15/ 24003151/bytedance- china- openai- microsoft- competitor-llm.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications https://www.theverge.com/2023/12/15/ 24003151/bytedance- china- openai- microsoft- competitor-llm

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This paper cites https://www.maye rbrown.com/en/insights/publications/2024/09/ca lifornia-passes-new-generative-artificial-inte lligence-law-requiring-disclosure-of-training- data.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications https://www.maye rbrown.com/en/insights/publications/2024/09/ca lifornia-passes-new-generative-artificial-inte lligence-law-requiring-disclosure-of-training- data

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications https://openai.com/index/introducing- canvas/

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications https://github.com/THUDM/ChatGLM3

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This paper cites https://platform.openai.com/docs/m odels/gpt-3-5-turbo.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications https://platform.openai.com/docs/m odels/gpt-3-5-turbo

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This paper cites https://platform.openai.com/docs/models/ gpt-4-turbo-and-gpt-4.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications https://platform.openai.com/docs/models/ gpt-4-turbo-and-gpt-4

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications https://hazy.com/

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications https://www.imdb.com/

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This paper cites https://leginfo.legislature.ca.gov /faces/billNavClient.xhtml?bill_id=202320240AB.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications https://leginfo.legislature.ca.gov /faces/billNavClient.xhtml?bill_id=202320240AB

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications https://ai.meta.com/llama/licens e/

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications https://huggingface.co/mis tralai/Mistral-7B-Instruct-v0.2

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications https://openai.com/policie s/business-terms/

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications https://openai.com/policies/row- terms-of-use/

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This paper cites An Empirical Study of Clinical Note Generation from Doctor-Patient Encounters.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications An Empirical Study of Clinical Note Generation from Doctor-Patient Encounters

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications On the difficulty of training Recurrent Neural Networks

Reference 16

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This paper cites METEOR: An Auto- matic Metric for MT Evaluation with Improved Correlation with Human Judgments.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications METEOR: An Auto- matic Metric for MT Evaluation with Improved Correlation with Human Judgments

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

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Comprehensive Exploration of Synthetic Data Generation: A Survey

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This paper cites Deciphering Textual Authenticity: A Generalized Strategy through the Lens of Large Language Semantics for Detecting Human vs. Machine-Generated Text.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Deciphering Textual Authenticity: A Generalized Strategy through the Lens of Large Language Semantics for Detecting Human vs. Machine-Generated Text

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Membership Inference Attacks From First Principles

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Dif- ferentially private sequential data publication via variable- length n-grams

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications GPT-Sentinel: Distinguishing Human and ChatGPT Generated Content

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Medically Aware GPT-3 as a Data Genera- tor for Medical Dialogue Summarization

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Synthetic Data: Methods, Use Cases, and Risks

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Under the Surface: Tracking the Artifactuality of LLM-Generated Data

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications BERT: Pre-training of Deep Bidirectional Trans- formers for Language Understanding

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challenges

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Feature-based detection of automated language models: tackling GPT-2, GPT-3 and Grover

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Bias and Fairness in Large Language Models: A Survey

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Gunter, and Nikita Borisov

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Self-Guided Noise-Free Data Genera- tion for Efficient Zero-Shot Learning

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Deep Learning

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Generative AI for Synthetic Data Generation: Methods, Challenges and the Future

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Eval- uating Large Language Models in Generating Synthetic HCI Research Data: a Case Study

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Deep Residual Learning for Image Recognition

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications MGTBench: Benchmarking Machine- Generated Text Detection

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Tar- geted Data Generation: Finding and Fixing Model Weak- nesses

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Synthetic data generation for tab- ular health records: A systematic review

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications On the Utility of Synthetic Data: An Empirical Evaluation on Machine Learning Tasks

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Yu, and Xuyun Zhang

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Survey of Hallucination in Natural Language Generation

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Exploiting Asymmetry for Synthetic Train- ing Data Generation: SynthIE and the Case of Information Extraction

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Analyzing and Reducing the Damage of Dataset Bias to Face Recognition With Synthetic Data

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications CQSumDP: A ChatGPT-Annotated Resource for Query-Focused Abstractive Summarization Based on Debatepedia

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Membership Leakage in Label- Only Exposures

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations

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Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications ROUGE: A Package for Automatic Evalua- tion of Summaries

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:43:57.175438Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e878fcbe-a296-49cc-9538-00f893afc377 · outbound

This paper cites Is Your Code Generated by ChatGPT Really Cor- rect? Rigorous Evaluation of Large Language Models for Code Generation.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Is Your Code Generated by ChatGPT Really Cor- rect? Rigorous Evaluation of Large Language Models for Code Generation

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:43:57.156385Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c209bcea-5a30-4183-9a3f-3148feb2a5c3 · outbound

This paper cites Low-Resource Court Judgment Summarization for Common Law Systems.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Low-Resource Court Judgment Summarization for Common Law Systems

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-08-09T17:43:56.290180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 65b92104-ebca-40db-b30e-ca3fd79450fc · outbound

This paper cites On the Detectability of ChatGPT Content: Benchmarking, Methodology, and Evaluation through the Lens of Academic Writing.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications On the Detectability of ChatGPT Content: Benchmarking, Methodology, and Evaluation through the Lens of Academic Writing

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-09T17:43:55.641382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 013ec051-627b-48ef-bc42-d8d56ffce7e8 · outbound

This paper cites Zero-Resource Hal- lucination Prevention for Large Language Models.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Zero-Resource Hal- lucination Prevention for Large Language Models

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:43:57.132862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1bd1b2e0-3bab-4345-b87e-e74cc7f3bfb4 · outbound

This paper cites Maas, Raymond E.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Maas, Raymond E

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:43:57.113405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 12d695e2-084e-4c3f-ac27-04574802992c · outbound

This paper cites JOBSKAPE: A Framework for Generating Synthetic Job Postings to Enhance Skill Matching.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications JOBSKAPE: A Framework for Generating Synthetic Job Postings to Enhance Skill Matching

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-09T17:43:55.658709Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c77dbd1-e4a5-4471-a801-b6f182b6cfe6 · outbound

This paper cites SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-09T17:43:55.663911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32a9a6f0-ec63-4b89-8e8c-f833986f1e0d · outbound

This paper cites Spam Filtering with Naive Bayes - Which Naive Bayes? In Conference on Email and Anti-Spam (CEAS).

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Spam Filtering with Naive Bayes - Which Naive Bayes? In Conference on Email and Anti-Spam (CEAS)

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:43:57.090719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 955f6c8d-38a9-4c5b-b465-fb8b09a6c5f0 · outbound

This paper cites Efficient Estimation of Word Representations in Vec- tor Space.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Efficient Estimation of Word Representations in Vec- tor Space

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:43:57.074549Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cd2dc536-3f3a-48e9-8896-fb7a910986a0 · outbound

This paper cites DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-09T17:43:55.680874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1786d7d-e41d-475a-b602-3ce9cdb4dece · outbound

This paper cites AgentInstruct: Toward Generative Teaching with Agentic Flows.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications AgentInstruct: Toward Generative Teaching with Agentic Flows

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-09T17:43:55.686565Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da5afc67-e60f-41cc-86ec-4b1e25453b8a · outbound

This paper cites The Parrot Dilemma: Human-Labeled vs.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications The Parrot Dilemma: Human-Labeled vs

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:43:57.056849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1413a291-8c57-4fde-8747-f688ce96d266 · outbound

This paper cites Enhanc- ing Automated Scoring of Math Self-Explanation Quality Using LLM-Generated Datasets: A Semi-Supervised Ap- proach.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Enhanc- ing Automated Scoring of Math Self-Explanation Quality Using LLM-Generated Datasets: A Semi-Supervised Ap- proach

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:43:57.038978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation eec64842-79a1-48c6-aee0-b76cb3babe56 · outbound

This paper cites Cohen, and Mirella Lapata.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Cohen, and Mirella Lapata

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:43:57.019096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 388b6155-8aa6-4b5c-82ca-cb4245772926 · outbound

This paper cites Ma- chine Learning with Membership Privacy using Adversarial Regularization.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Ma- chine Learning with Membership Privacy using Adversarial Regularization

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:43:56.999722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b9bd500d-9ac8-46f7-9824-b573de73d5d0 · outbound

This paper cites Tieu, Huy H.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Tieu, Huy H

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:43:56.983099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3f0dbde2-8521-416f-94b1-822c71ecd4df · outbound

This paper cites Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:43:56.967683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 55c6776f-ce1b-4da0-a725-4b84debf41f2 · outbound

This paper cites Bleu: a Method for Automatic Evaluation of Machine Translation.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Bleu: a Method for Automatic Evaluation of Machine Translation

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:43:56.950623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bb36a4f9-b1c2-447c-b21b-21d2ec668510 · outbound

This paper cites Smith, Nima M.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Smith, Nima M

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:43:56.935121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8945a1df-1813-4362-8b1b-2401ba8fc060 · outbound

This paper cites an unresolved cited work.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-08-09T17:43:56.915367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 63b388bb-ea4c-4533-87f7-68541ad7762b · outbound

This paper cites Boost- ing Instance Segmentation with Synthetic Data: A study to overcome the limits of real world data sets.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Boost- ing Instance Segmentation with Synthetic Data: A study to overcome the limits of real world data sets

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:43:56.893899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5fd87c05-77f8-4462-96e2-5cc69f29b935 · outbound

This paper cites Language Models are Unsuper- vised Multitask Learners.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Language Models are Unsuper- vised Multitask Learners

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:43:56.877066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3117a810-f580-4f97-ba64-971cdc33b5ba · outbound

This paper cites A Survey of Hallucination in Large Foundation Models.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications A Survey of Hallucination in Large Foundation Models

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-09T17:43:55.757035Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e4a14c5-fa14-488b-9a05-9dc01eda3dfc · outbound

This paper cites ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:43:56.859917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6634a6a1-5ae4-437f-8645-15d9ebc28af7 · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-09T17:43:55.769290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebf25f35-cd09-4c02-be3a-e80b7a4c87b0 · outbound

This paper cites Liu, and Christopher D.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Liu, and Christopher D

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:43:56.838768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cda93921-c634-468f-9a54-641a68b40ad6 · outbound

This paper cites In ChatGPT We Trust? Measuring and Characterizing the Reliability of ChatGPT.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications In ChatGPT We Trust? Measuring and Characterizing the Reliability of ChatGPT

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-09T17:43:55.781072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b1c9eea-c283-44bc-b829-7b498df5b234 · outbound

This paper cites Membership Inference Attacks Against Machine Learning Models.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Membership Inference Attacks Against Machine Learning Models

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:43:56.821537Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 27e5076d-b7c3-4cf3-8245-84780ecbeab9 · outbound

This paper cites Systematic Evaluation of Privacy Risks of Machine Learning Models.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Systematic Evaluation of Privacy Risks of Machine Learning Models

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:43:56.799057Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d63531d7-faee-4afe-a768-059df764ea6b · outbound

This paper cites Does Synthetic Data Generation of LLMs Help Clinical Text Mining?.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Does Synthetic Data Generation of LLMs Help Clinical Text Mining?

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-09T17:43:55.797446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec1208c2-d0c8-49e9-915c-15fb58b04b93 · outbound

This paper cites Large language models in medicine.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Large language models in medicine

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:43:56.780175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5700e076-88da-49a5-a5c0-52a343197fbd · outbound

This paper cites Factoring Variations in Natural Images with Deep Gaussian Mixture Models.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Factoring Variations in Natural Images with Deep Gaussian Mixture Models

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:43:56.764146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8c3a692d-c169-4524-86d3-9c21d7b0eff7 · outbound

This paper cites Visualizing Data using t-SNE.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Visualizing Data using t-SNE

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-09T17:43:55.812781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6dec13f8-3d3f-4889-9e7c-a22252ca1aaa · outbound

This paper cites Gomez, Lukasz Kaiser, and Illia Polosukhin.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Gomez, Lukasz Kaiser, and Illia Polosukhin

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:43:56.720171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 65e32dd5-c7bb-4215-b9b3-da6c8f226e0d · outbound

This paper cites an unresolved cited work.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Unresolved cited work

Reference 87

Resolution: unresolved; raw_fallback, observed 2026-08-09T17:43:56.693389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9f983213-f8d6-4f1e-83d5-f05881220eb9 · outbound

This paper cites M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-09T17:43:55.828034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10f1ca84-fba8-4fe8-bc9b-1e3823638ff0 · outbound

This paper cites Quantifying Privacy Risks of Prompts in Visual Prompt Learning.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Quantifying Privacy Risks of Prompts in Visual Prompt Learning

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:43:56.675330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9ebcbe38-db00-42e8-844f-523c3c14b624 · outbound

This paper cites Membership Inference Attacks Against Text-to-image Generation Models.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Membership Inference Attacks Against Text-to-image Generation Models

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-09T17:43:55.838647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d544d047-0b67-4cfb-8265-91f012238fb0 · outbound

This paper cites Towards Auditing Large Language Models: Improving Text-based Stereotype Detection.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Towards Auditing Large Language Models: Improving Text-based Stereotype Detection

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-09T17:43:55.843374Z

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Unavailable: canonical work link unavailable.

Observation 75c0c7f9-d417-49ee-a014-48b79075fe02 · outbound

This paper cites Fairness Feedback Loops: Training on Synthetic Data Am- plifies Bias.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Fairness Feedback Loops: Training on Synthetic Data Am- plifies Bias

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:43:56.656019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7f5a35eb-a20b-49e4-b249-0adef0c799eb · outbound

This paper cites Woodpecker: Hallucination Correction for Multimodal Large Language Models.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Woodpecker: Hallucination Correction for Multimodal Large Language Models

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-09T17:43:55.852604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 103adfe6-0912-4b1b-9e70-4e613fb9a700 · outbound

This paper cites A Plot is Worth a Thousand Words: Model Information Stealing Attacks via Scientific Plots.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications A Plot is Worth a Thousand Words: Model Information Stealing Attacks via Scientific Plots

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:43:56.638347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ed8bbb30-29e7-44fd-ac58-769c7625254e · outbound

This paper cites an unresolved cited work.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Unresolved cited work

Reference 95

Resolution: unresolved; raw_fallback, observed 2026-08-09T17:43:56.617154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e541c8aa-6ff5-409e-9571-b75f4aacbb04 · outbound

This paper cites Wein- berger, and Yoav Artzi.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Wein- berger, and Yoav Artzi

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:43:56.590331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 09cf95d7-3602-4d50-9c97-396d4190ee85 · outbound

This paper cites Character- level Convolutional Networks for Text Classification.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Character- level Convolutional Networks for Text Classification

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:43:56.570004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fea8a66b-95a2-4f17-83a9-009ee76bb6af · outbound

This paper cites Large Language Models for Time Series: A Survey.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Large Language Models for Time Series: A Survey

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-09T17:43:55.876456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e599544c-b5d9-4238-9d83-e3ae19aba3ac · outbound

This paper cites Large Language Models for Scientific Synthesis, Inference and Explanation.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications Large Language Models for Scientific Synthesis, Inference and Explanation

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-09T17:43:55.881130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3da62e0-00a3-44aa-baa4-780edad82762 · outbound

This paper cites A Survey on Data Augmentation in Large Model Era.

Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications A Survey on Data Augmentation in Large Model Era

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-09T17:43:55.886061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Pith citing papers