Measuring Diversity in Synthetic Datasets | Paper Citation Record

Observation 220944f6-2a3a-4268-b3af-10d298f7bd51 · outbound

This paper cites Synthetic Dialogue Dataset Generation using LLM Agents.

Measuring Diversity in Synthetic Datasets Synthetic Dialogue Dataset Generation using LLM Agents

Reference 1

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Observation 33b37dff-0c70-4d11-bf3b-2ec8044925ce · outbound

This paper cites GPT-4 Technical Report.

Measuring Diversity in Synthetic Datasets GPT-4 Technical Report

Reference 2

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Observation ddd899f2-38a9-4b83-a2e7-155f92c4297b · outbound

This paper cites User's guide to correlation coefficients.

Measuring Diversity in Synthetic Datasets User's guide to correlation coefficients

Reference 3

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Observation b713cd28-5825-431d-b703-e46bd8d326f5 · outbound

This paper cites J., Kragic, D., and Kjellstrom, H.

Measuring Diversity in Synthetic Datasets J., Kragic, D., and Kjellstrom, H

Reference 4

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Observation a1b16900-89c2-41e6-8d5d-7b610312a26b · outbound

This paper cites Language GANs Falling Short.

Measuring Diversity in Synthetic Datasets Language GANs Falling Short

Reference 5

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Observation ee8b96bc-0712-46a4-a494-6b2c784d719b · outbound

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

Measuring Diversity in Synthetic Datasets Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 6

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Observation 1039d1ff-8d5c-444f-acab-dbdda528b7cd · outbound

This paper cites Mixture of Soft Prompts for Controllable Data Generation.

Measuring Diversity in Synthetic Datasets Mixture of Soft Prompts for Controllable Data Generation

Reference 7

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Observation 4e65a109-73cf-4999-81e5-2b4fc6becbf0 · outbound

This paper cites Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions.

Measuring Diversity in Synthetic Datasets Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions

Reference 8

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Observation 22c18fa7-488b-44e3-8585-27edaebe1941 · outbound

This paper cites Eval all, trust a few, do wrong to none: Comparing sentence generation models.

Measuring Diversity in Synthetic Datasets Eval all, trust a few, do wrong to none: Comparing sentence generation models

Reference 9

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Observation 211c37db-37b3-4010-bbd9-b03727702b09 · outbound

This paper cites RandAugment: Practical automated data augmentation with a reduced search space.

Measuring Diversity in Synthetic Datasets RandAugment: Practical automated data augmentation with a reduced search space

Reference 10

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Observation 96a2b178-555d-48c2-b330-083355af2d2e · outbound

This paper cites AugGPT: Leveraging ChatGPT for Text Data Augmentation.

Measuring Diversity in Synthetic Datasets AugGPT: Leveraging ChatGPT for Text Data Augmentation

Reference 11

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Observation fb61bb52-20ac-4cdd-a411-305a0b4102a8 · outbound

This paper cites and Dieng, A.

Measuring Diversity in Synthetic Datasets and Dieng, A

Reference 12

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Observation 55094d90-8963-49b9-acd4-b9aaf9212b41 · outbound

This paper cites The mnist database of handwritten digit images for machine learning research [best of the web].

Measuring Diversity in Synthetic Datasets The mnist database of handwritten digit images for machine learning research [best of the web]

Reference 13

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Observation ea37a12b-be95-45ef-b602-e714b715632c · outbound

This paper cites Prescribed Generative Adversarial Networks.

Measuring Diversity in Synthetic Datasets Prescribed Generative Adversarial Networks

Reference 14

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Observation 8a45b919-42cd-47d5-bdfc-d37d4c40ef3c · outbound

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

Measuring Diversity in Synthetic Datasets Is GPT-3 a Good Data Annotator?

Reference 15

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Observation 7d071908-c6e2-4c94-a979-3b36db85b412 · outbound

This paper cites Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challenges.

Measuring Diversity in Synthetic Datasets Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challenges

Reference 16

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Observation 2ee59089-5171-43f0-a8c9-ee723562319f · outbound

This paper cites G., Santos, G.

Measuring Diversity in Synthetic Datasets G., Santos, G

Reference 17

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Observation 9fa8936f-eca7-4e64-b27d-0062ec69e5d7 · outbound

This paper cites and Black, A.

Measuring Diversity in Synthetic Datasets and Black, A

Reference 18

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Observation a8a656e8-9f6a-4981-9034-7d86b5c7725a · outbound

This paper cites CoDa: Constrained Generation based Data Augmentation for Low-Resource NLP.

Measuring Diversity in Synthetic Datasets CoDa: Constrained Generation based Data Augmentation for Low-Resource NLP

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-08T04:54:51.713193Z

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Observation 61899b9b-774b-4b2c-b2ad-2917f3493b17 · outbound

This paper cites SimCSE: Simple Contrastive Learning of Sentence Embeddings.

Measuring Diversity in Synthetic Datasets SimCSE: Simple Contrastive Learning of Sentence Embeddings

Reference 20

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Observation c1cd2522-4312-40ef-a3cd-8bdec0f9846e · outbound

This paper cites Chatgpt outperforms crowd workers for text-annotation tasks.

Measuring Diversity in Synthetic Datasets Chatgpt outperforms crowd workers for text-annotation tasks

Reference 21

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Observation 084c08f0-a888-4ff8-90ae-1fd37e3d913d · outbound

This paper cites Affinity and Diversity: Quantifying Mechanisms of Data Augmentation.

Measuring Diversity in Synthetic Datasets Affinity and Diversity: Quantifying Mechanisms of Data Augmentation

Reference 22

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Observation 8cd9f28b-0a3c-4fc1-b39e-74cad6da37cb · outbound

This paper cites Generative adversarial networks.

Measuring Diversity in Synthetic Datasets Generative adversarial networks

Reference 23

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Observation a48b1012-d602-4e85-9d11-5a42183aa614 · outbound

This paper cites Llm-based code generation method for golang compiler testing.

Measuring Diversity in Synthetic Datasets Llm-based code generation method for golang compiler testing

Reference 24

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Observation 3cc466e1-a7d5-4c7c-9688-b485cfe481aa · outbound

This paper cites TarGEN: Targeted Data Generation with Large Language Models.

Measuring Diversity in Synthetic Datasets TarGEN: Targeted Data Generation with Large Language Models

Reference 25

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Observation 6ff78ebf-fc4f-4f48-9253-fe023c4327e1 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.

Measuring Diversity in Synthetic Datasets Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 26

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Observation ced880b2-55ff-43b1-a95e-ab19c090c027 · outbound

This paper cites The Curious Case of Neural Text Degeneration.

Measuring Diversity in Synthetic Datasets The Curious Case of Neural Text Degeneration

Reference 27

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Observation 1eccecd3-c955-44ef-8cf5-3207bb0f7e76 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Measuring Diversity in Synthetic Datasets LoRA: Low-Rank Adaptation of Large Language Models

Reference 28

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Observation 915adbf8-21d4-40ec-a665-d8ba59553642 · outbound

This paper cites Learning preference model for llms via automatic preference data generation.

Measuring Diversity in Synthetic Datasets Learning preference model for llms via automatic preference data generation

Reference 29

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Observation a4504023-31d4-4aed-aeb8-3c8a7fd6ecba · outbound

This paper cites T., Boutros, F., Kuijper, A., and Damer, N.

Measuring Diversity in Synthetic Datasets T., Boutros, F., Kuijper, A., and Damer, N

Reference 30

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Observation 346789cd-e5e3-4992-a165-e28b7292b4f0 · outbound

This paper cites T., and Farnia, F.

Measuring Diversity in Synthetic Datasets T., and Farnia, F

Reference 31

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Observation 628f6680-fc97-4ed4-aa59-f92dadd7ffdf · outbound

This paper cites an unresolved cited work.

Measuring Diversity in Synthetic Datasets Unresolved cited work

Reference 32

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Observation 651b66e6-6334-445d-ada8-afd0f68e56bd · outbound

This paper cites Natural language processing: state of the art, current trends and challenges.

Measuring Diversity in Synthetic Datasets Natural language processing: state of the art, current trends and challenges

Reference 33

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Observation cb944758-1731-4815-ac6e-bdb5fcb46e64 · outbound

This paper cites Improved precision and recall metric for assessing generative models.

Measuring Diversity in Synthetic Datasets Improved precision and recall metric for assessing generative models

Reference 34

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Observation 9d304498-f4ba-44eb-828d-51e78930e753 · outbound

This paper cites Diversity, Density, and Homogeneity: Quantitative Characteristic Metrics for Text Collections.

Measuring Diversity in Synthetic Datasets Diversity, Density, and Homogeneity: Quantitative Characteristic Metrics for Text Collections

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-08-08T04:54:51.612340Z

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Observation 8484a75b-fa89-4073-97c1-e32949eb63f8 · outbound

This paper cites Exploring precision and recall to assess the quality and diversity of llms.

Measuring Diversity in Synthetic Datasets Exploring precision and recall to assess the quality and diversity of llms

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:52.315626Z

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Observation f44d8e89-32e9-4eb4-b590-e5bac41db550 · outbound

This paper cites Beyond Scale: The Diversity Coefficient as a Data Quality Metric for Variability in Natural Language Data.

Measuring Diversity in Synthetic Datasets Beyond Scale: The Diversity Coefficient as a Data Quality Metric for Variability in Natural Language Data

Reference 37

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Observation 45235e71-9799-41c9-9693-2e61316d9e64 · outbound

This paper cites Graddiv: Adversarial robustness of randomized neural networks via gradient diversity regularization.

Measuring Diversity in Synthetic Datasets Graddiv: Adversarial robustness of randomized neural networks via gradient diversity regularization

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:52.298118Z

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Observation 804556e7-fc75-4fc8-907e-b6f0689e85e9 · outbound

This paper cites and Cobbold, C.

Measuring Diversity in Synthetic Datasets and Cobbold, C

Reference 39

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Observation 578b16a5-6ed1-49ee-9417-6dab5b4b436f · outbound

This paper cites Rich feature learning via diversification.

Measuring Diversity in Synthetic Datasets Rich feature learning via diversification

Reference 40

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Observation 0fef96c9-0761-4ec1-a853-e6bc24548524 · outbound

This paper cites A Diversity-Promoting Objective Function for Neural Conversation Models.

Measuring Diversity in Synthetic Datasets A Diversity-Promoting Objective Function for Neural Conversation Models

Reference 41

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Observation 29c9d34d-3fc1-4e14-bd16-94ef907260cd · outbound

This paper cites Empowering Large Language Models for Textual Data Augmentation.

Measuring Diversity in Synthetic Datasets Empowering Large Language Models for Textual Data Augmentation

Reference 42

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Observation c3141f0c-ebb9-45f5-aff6-ab04f5dbdf30 · outbound

This paper cites Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations.

Measuring Diversity in Synthetic Datasets Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations

Reference 43

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This paper cites Data Augmentation for Text-based Person Retrieval Using Large Language Models.

Measuring Diversity in Synthetic Datasets Data Augmentation for Text-based Person Retrieval Using Large Language Models

Reference 44

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Observation 08eb5c24-8535-4b84-8652-abfed5201ad3 · outbound

This paper cites Holistic Evaluation of Language Models.

Measuring Diversity in Synthetic Datasets Holistic Evaluation of Language Models

Reference 45

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This paper cites Diverse image generation via self-conditioned gans.

Measuring Diversity in Synthetic Datasets Diverse image generation via self-conditioned gans

Reference 46

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Observation c377feb5-181f-4ea6-9368-b55998c56af2 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Measuring Diversity in Synthetic Datasets RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 47

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

This paper cites On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey.

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

Reference 48

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Observation 51b4ad43-6ec9-49af-8b38-a22dfca35cad · outbound

This paper cites Decoupled Weight Decay Regularization.

Measuring Diversity in Synthetic Datasets Decoupled Weight Decay Regularization

Reference 49

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Observation 2a577f0d-9ae0-483f-89c5-00da8c9dfefc · outbound

This paper cites L., Daly, R.

Measuring Diversity in Synthetic Datasets L., Daly, R

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:52.234749Z

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Observation b2722620-46bc-4702-b72f-264135027aea · outbound

This paper cites Zero-Shot Stance Detection using Contextual Data Generation with LLMs.

Measuring Diversity in Synthetic Datasets Zero-Shot Stance Detection using Contextual Data Generation with LLMs

Reference 51

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Observation 3f562b54-5973-460e-8ce2-4eb8e0b8b603 · outbound

This paper cites Online normalizer calculation for softmax.

Measuring Diversity in Synthetic Datasets Online normalizer calculation for softmax

Reference 52

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Observation 805fea4b-e9e7-4120-a3af-3544770927cb · outbound

This paper cites DQI: Measuring Data Quality in NLP.

Measuring Diversity in Synthetic Datasets DQI: Measuring Data Quality in NLP

Reference 53

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Observation 8d061d46-68a1-4e8d-bcd2-de6c802f0cf8 · outbound

This paper cites A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories.

Measuring Diversity in Synthetic Datasets A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-08T04:54:50.811177Z

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Observation f30006b6-8d5d-4116-928a-e05759fb6821 · outbound

This paper cites F., Oh, S.

Measuring Diversity in Synthetic Datasets F., Oh, S

Reference 55

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Observation d145c66b-c379-4127-bc57-5a43002e8588 · outbound

This paper cites Towards a Scalable Reference-Free Evaluation of Generative Models.

Measuring Diversity in Synthetic Datasets Towards a Scalable Reference-Free Evaluation of Generative Models

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-08T04:54:51.371491Z

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Observation 354c82d9-8399-46c0-bb0a-40064856b603 · outbound

This paper cites Does Writing with Language Models Reduce Content Diversity?.

Measuring Diversity in Synthetic Datasets Does Writing with Language Models Reduce Content Diversity?

Reference 57

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Observation 2111c041-ac5b-4b68-afe0-8a2460f2ce45 · outbound

This paper cites SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition.

Measuring Diversity in Synthetic Datasets SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

Reference 58

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Observation f7dd0437-fdb8-422c-bb14-4dc6a717b374 · outbound

This paper cites Mauve: Measuring the gap between neural text and human text using divergence frontiers.

Measuring Diversity in Synthetic Datasets Mauve: Measuring the gap between neural text and human text using divergence frontiers

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:52.206253Z

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Observation ac6b8b22-79d9-42c1-8efb-bce621c7ee56 · outbound

This paper cites unsup-simcse-bert-base-uncased, 2021.

Measuring Diversity in Synthetic Datasets unsup-simcse-bert-base-uncased, 2021

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:52.187494Z

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Observation 2e4d732b-9b18-4896-acf7-1fbc98a19208 · outbound

This paper cites an unresolved cited work.

Measuring Diversity in Synthetic Datasets Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-08-08T04:54:52.171641Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 07a3c404-afc1-4a17-beb5-aebb845cb6e0 · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

Measuring Diversity in Synthetic Datasets Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 62

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Observation c6fbf12b-d000-483f-9986-45df04151659 · outbound

This paper cites Beyond Accuracy: Behavioral Testing of NLP models with CheckList.

Measuring Diversity in Synthetic Datasets Beyond Accuracy: Behavioral Testing of NLP models with CheckList

Reference 63

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Observation fb9834d8-84f6-4af4-82a7-d909508911db · outbound

This paper cites A Guide To Effectively Leveraging LLMs for Low-Resource Text Summarization: Data Augmentation and Semi-supervised Approaches.

Measuring Diversity in Synthetic Datasets A Guide To Effectively Leveraging LLMs for Low-Resource Text Summarization: Data Augmentation and Semi-supervised Approaches

Reference 64

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This paper cites Can LLMs Augment Low-Resource Reading Comprehension Datasets? Opportunities and Challenges.

Measuring Diversity in Synthetic Datasets Can LLMs Augment Low-Resource Reading Comprehension Datasets? Opportunities and Challenges

Reference 65

Resolution: verified exact; local_arxiv, observed 2026-08-08T04:54:51.268156Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a9439b37-f7ab-4139-8dcc-1787ffcb6881 · outbound

This paper cites Gaussian processes for machine learning.

Measuring Diversity in Synthetic Datasets Gaussian processes for machine learning

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:52.155689Z

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Observation 45baff46-d1eb-4867-b7ab-44538bfb9d78 · outbound

This paper cites Svd-softmax: Fast softmax approximation on large vocabulary neural networks.

Measuring Diversity in Synthetic Datasets Svd-softmax: Fast softmax approximation on large vocabulary neural networks

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:52.139912Z

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Observation 2af6fa1c-a014-467f-9909-16861cea5fa6 · outbound

This paper cites Generating diverse translations with sentence codes.

Measuring Diversity in Synthetic Datasets Generating diverse translations with sentence codes

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:52.123971Z

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Observation 2697c452-7575-4cf6-ba83-7ea9ee50d6a7 · outbound

This paper cites D., Ng, A.

Measuring Diversity in Synthetic Datasets D., Ng, A

Reference 69

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Observation c19e820d-7ad4-4522-96b7-789df23b3d95 · outbound

This paper cites Scaling Data Diversity for Fine-Tuning Language Models in Human Alignment.

Measuring Diversity in Synthetic Datasets Scaling Data Diversity for Fine-Tuning Language Models in Human Alignment

Reference 70

Resolution: verified exact; local_arxiv, observed 2026-08-08T04:54:51.246391Z

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This paper cites The proof and measurement of association between two things.

Measuring Diversity in Synthetic Datasets The proof and measurement of association between two things

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-08T04:54:50.890842Z

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This paper cites Semantic Diversity in Dialogue with Natural Language Inference.

Measuring Diversity in Synthetic Datasets Semantic Diversity in Dialogue with Natural Language Inference

Reference 72

Resolution: verified exact; local_arxiv, observed 2026-08-08T04:54:51.222877Z

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Observation ea70da3b-b42c-4fa6-baf4-943670a7bdbf · outbound

This paper cites Large Language Models for Data Annotation and Synthesis: A Survey.

Measuring Diversity in Synthetic Datasets Large Language Models for Data Annotation and Synthesis: A Survey

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-08T04:54:50.899877Z

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Unavailable: canonical work link unavailable.

Observation a699ce9e-52f9-46ce-819f-6c8aecf959bc · outbound

This paper cites Alpacaeval : An automatic evaluator for instruction-following language models, 2023.

Measuring Diversity in Synthetic Datasets Alpacaeval : An automatic evaluator for instruction-following language models, 2023

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:52.087061Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 629a0c19-c58a-4db8-aadd-f2f2f2dd353a · outbound

This paper cites Evaluating the Evaluation of Diversity in Natural Language Generation.

Measuring Diversity in Synthetic Datasets Evaluating the Evaluation of Diversity in Natural Language Generation

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-08T04:54:50.910077Z

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Unavailable: canonical work link unavailable.

Observation a6e1cf4b-9756-47ce-a475-3a0c3c18005c · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Measuring Diversity in Synthetic Datasets Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-08T04:54:50.914807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59281473-afc1-4104-a287-32a7b580bdf8 · outbound

This paper cites Self-Instruct: Aligning Language Models with Self-Generated Instructions.

Measuring Diversity in Synthetic Datasets Self-Instruct: Aligning Language Models with Self-Generated Instructions

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-08T04:54:50.919460Z

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Observation 6a9e1682-7dba-4b27-a969-7c416a361088 · outbound

This paper cites J., and Sch \"o lkopf, B.

Measuring Diversity in Synthetic Datasets J., and Sch \"o lkopf, B

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:52.070244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bcf63371-685d-4431-83c7-13ada72e12e3 · outbound

This paper cites A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT.

Measuring Diversity in Synthetic Datasets A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-08T04:54:50.929615Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94a79fb1-0db3-49d0-a84b-4974bbcf0e3c · outbound

This paper cites Investigating the effectiveness of data augmentation from similarity and diversity: An empirical study.

Measuring Diversity in Synthetic Datasets Investigating the effectiveness of data augmentation from similarity and diversity: An empirical study

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:52.052711Z

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Observation 880b9266-a922-4e8b-aac1-b01015cfcb49 · outbound

This paper cites Mini-da: Improving your model performance through minimal data augmentation using llm.

Measuring Diversity in Synthetic Datasets Mini-da: Improving your model performance through minimal data augmentation using llm

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:52.036032Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4dcc5eaf-4c49-46da-b656-b0dbfaeca161 · outbound

This paper cites ZeroGen: Efficient Zero-shot Learning via Dataset Generation.

Measuring Diversity in Synthetic Datasets ZeroGen: Efficient Zero-shot Learning via Dataset Generation

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-08T04:54:50.944354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b8dba37-0569-4a98-a88e-6db513a20450 · outbound

This paper cites LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition.

Measuring Diversity in Synthetic Datasets LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-08T04:54:50.949266Z

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Unavailable: canonical work link unavailable.

Observation cd503249-e67f-4711-b802-1776c985488d · outbound

This paper cites GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation.

Measuring Diversity in Synthetic Datasets GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-08T04:54:50.954005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69c113c1-d05b-4243-a9dc-b25ffc8b27c0 · outbound

This paper cites Seqgan: Sequence generative adversarial nets with policy gradient.

Measuring Diversity in Synthetic Datasets Seqgan: Sequence generative adversarial nets with policy gradient

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:52.019673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 31a7f6a2-08bc-4d0b-a2ab-688396fce563 · outbound

This paper cites J., Krishna, R., Shen, J., and Zhang, C.

Measuring Diversity in Synthetic Datasets J., Krishna, R., Shen, J., and Zhang, C

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:52.002941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a8947880-ad2f-4cd3-a15c-9386aa3be716 · outbound

This paper cites Large language models for healthcare data augmentation: An example on patient-trial matching.

Measuring Diversity in Synthetic Datasets Large language models for healthcare data augmentation: An example on patient-trial matching

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:51.986380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 165f613f-8b50-463b-87c7-d8edfa19b879 · outbound

This paper cites Advancing LLM Reasoning Generalists with Preference Trees.

Measuring Diversity in Synthetic Datasets Advancing LLM Reasoning Generalists with Preference Trees

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-08T04:54:50.972531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42718af5-05cf-4a99-acc0-3ac70493bd96 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Measuring Diversity in Synthetic Datasets mixup: Beyond Empirical Risk Minimization

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-08T04:54:50.977285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12b56394-359d-4ae8-9b6c-c062538ba17e · outbound

This paper cites Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning.

Measuring Diversity in Synthetic Datasets Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-08T04:54:50.982220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f7c0f15-f21a-48c9-8168-8846f4aa3635 · outbound

This paper cites Character-level convolutional networks for text classification.

Measuring Diversity in Synthetic Datasets Character-level convolutional networks for text classification

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-08T04:54:50.986785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 137d54a6-ebf9-4298-a731-caee5ee8c2eb · outbound

This paper cites Bootstrapped unsupervised sentence representation learning.

Measuring Diversity in Synthetic Datasets Bootstrapped unsupervised sentence representation learning

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:51.959354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d2aa0cc3-244a-4a9b-94bf-f00bdbaaf93b · outbound

This paper cites Texygen: A benchmarking platform for text generation models.

Measuring Diversity in Synthetic Datasets Texygen: A benchmarking platform for text generation models

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:54:51.942160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9d0ced15-e46a-4bab-8035-e1c0c9b1adbd · outbound

This paper cites write newline.

Measuring Diversity in Synthetic Datasets write newline

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-08T04:54:51.000155Z

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