Less is More: Adaptive Coverage for Synthetic Training Data | Paper Citation Record

Observation 7b5b5225-59fc-49b9-85e0-ce62df844a8f · outbound

This paper cites GPT-4 Technical Report.

Less is More: Adaptive Coverage for Synthetic Training Data GPT-4 Technical Report

Reference 1

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Observation a190d634-5a69-457a-91aa-8529500d2f14 · outbound

This paper cites A Survey on Data Selection for Language Models.

Less is More: Adaptive Coverage for Synthetic Training Data A Survey on Data Selection for Language Models

Reference 2

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Observation 0fbc250d-2f94-43a7-860b-2e8883912d14 · outbound

This paper cites Language Models are Few-Shot Learners.

Less is More: Adaptive Coverage for Synthetic Training Data Language Models are Few-Shot Learners

Reference 3

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Observation 8174cfde-dca2-45d3-acf1-aedbd9aebf54 · outbound

This paper cites Why it is hard to find ai in smes: A survey from the practice and how to promote it.

Less is More: Adaptive Coverage for Synthetic Training Data Why it is hard to find ai in smes: A survey from the practice and how to promote it

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:51:08.790582Z

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Observation 4d9ed737-f3f8-4a26-bae3-d1621ee62370 · outbound

This paper cites Stars: Tera-scale graph building for clustering and learning.Advances in Neural Information Processing Systems 35 (2022), 21470–21481.

Less is More: Adaptive Coverage for Synthetic Training Data Stars: Tera-scale graph building for clustering and learning.Advances in Neural Information Processing Systems 35 (2022), 21470–21481

Reference 5

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Observation 6e2926f9-6ca6-4831-b9ea-b28eb3bb28c7 · outbound

This paper cites AlpaGasus: Training A Better Alpaca with Fewer Data.

Less is More: Adaptive Coverage for Synthetic Training Data AlpaGasus: Training A Better Alpaca with Fewer Data

Reference 6

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Observation ea33a409-b6b2-44fb-810a-129490e80e7e · outbound

This paper cites Selection via Proxy: Efficient Data Selection for Deep Learning.

Less is More: Adaptive Coverage for Synthetic Training Data Selection via Proxy: Efficient Data Selection for Deep Learning

Reference 7

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Observation f5723806-0220-4bfd-8e28-c494a745b050 · outbound

This paper cites When low resource nlp meets unsupervised language model: Meta-pretraining then meta-learning for few-shot text classification (student abstract).

Less is More: Adaptive Coverage for Synthetic Training Data When low resource nlp meets unsupervised language model: Meta-pretraining then meta-learning for few-shot text classification (student abstract)

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:51:08.768238Z

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Observation bc9e3980-9323-4795-a86f-034d63cd43b7 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Less is More: Adaptive Coverage for Synthetic Training Data BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 9

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Observation 1b9957e8-a81a-4729-aa93-763416343d34 · outbound

This paper cites DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks.

Less is More: Adaptive Coverage for Synthetic Training Data DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks

Reference 10

Resolution: metadata mismatch; local_arxiv, observed 2026-08-16T11:51:08.397654Z

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Observation 196dd62c-c81e-4bfe-9c82-843e86eed898 · outbound

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

Less is More: Adaptive Coverage for Synthetic Training Data Is GPT-3 a Good Data Annotator?

Reference 11

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Observation fd744923-eb6d-4517-9392-ba6a0a9c2593 · outbound

This paper cites Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping.

Less is More: Adaptive Coverage for Synthetic Training Data Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 12

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Observation e29ea948-0d18-4662-9548-32565161da42 · outbound

This paper cites Clustering for private interest-based advertising.

Less is More: Adaptive Coverage for Synthetic Training Data Clustering for private interest-based advertising

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:51:08.757918Z

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Observation b4729c0c-1468-4e3b-ae9e-ba8fa4fc3624 · outbound

This paper cites A threshold of ln n for approximating set cover.Journal of the ACM (JACM) 45, 4 (1998), 634–652.

Less is More: Adaptive Coverage for Synthetic Training Data A threshold of ln n for approximating set cover.Journal of the ACM (JACM) 45, 4 (1998), 634–652

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:51:08.746507Z

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Observation b4e186af-f061-4650-9ed3-4f513cb397d6 · outbound

This paper cites Better Synthetic Data by Retrieving and Transforming Existing Datasets.

Less is More: Adaptive Coverage for Synthetic Training Data Better Synthetic Data by Retrieving and Transforming Existing Datasets

Reference 15

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Observation 6ae11299-c285-4544-888c-ed44f68efbd6 · outbound

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

Less is More: Adaptive Coverage for Synthetic Training Data Chatgpt outperforms crowd workers for text-annotation tasks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:51:08.735806Z

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Observation fc25bfe9-4928-4d9e-871f-aa3b30e9712b · outbound

This paper cites Domain adaptation for large-scale sentiment classification: A deep learning approach.

Less is More: Adaptive Coverage for Synthetic Training Data Domain adaptation for large-scale sentiment classification: A deep learning approach

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:51:08.725898Z

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Observation 86ffc323-3185-40b9-bd8f-74d7cb621175 · outbound

This paper cites Deepcore: A comprehensive library for coreset selection in deep learning.

Less is More: Adaptive Coverage for Synthetic Training Data Deepcore: A comprehensive library for coreset selection in deep learning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:51:08.715071Z

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Observation 93c6b446-ae50-40b5-986d-9757ae8838ba · outbound

This paper cites Grale: Designing networks for graph learning.

Less is More: Adaptive Coverage for Synthetic Training Data Grale: Designing networks for graph learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:51:08.704961Z

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Observation 9715d686-5095-4e67-bcf7-d953ecc62cc1 · outbound

This paper cites FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation.

Less is More: Adaptive Coverage for Synthetic Training Data FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation

Reference 20

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Observation 4391a698-73e7-425e-8d53-00b2d3cfc903 · outbound

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

Less is More: Adaptive Coverage for Synthetic Training Data Synthetic Data in AI: Challenges, Applications, and Ethical Implications

Reference 21

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Observation 76786112-a3e3-416b-a732-acd39dbd7c0b · outbound

This paper cites Toxigen: A large-scale machine-generated dataset for adversarial and implicit hate speech detection.

Less is More: Adaptive Coverage for Synthetic Training Data Toxigen: A large-scale machine-generated dataset for adversarial and implicit hate speech detection

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:51:08.694252Z

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Observation 9b7dc751-04cf-4ab2-b7bf-94b97c0ba3a6 · outbound

This paper cites On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation.

Less is More: Adaptive Coverage for Synthetic Training Data On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation

Reference 23

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Observation d6182c96-a203-47e2-b3c2-1f4be4187eff · outbound

This paper cites an unresolved cited work.

Less is More: Adaptive Coverage for Synthetic Training Data Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:51:08.681672Z

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Observation 917dd301-a410-4675-866d-dba371ff1f45 · outbound

This paper cites Human feedback is not gold standard.

Less is More: Adaptive Coverage for Synthetic Training Data Human feedback is not gold standard

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:51:08.671778Z

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Observation 8cc7799e-f13c-4441-b69d-840580e329dd · outbound

This paper cites W., and Liang, P.

Less is More: Adaptive Coverage for Synthetic Training Data W., and Liang, P

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:51:08.662473Z

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Observation 09f08d13-b70d-4560-b0a0-7b0baf288986 · outbound

This paper cites Harnessing large-language models to generate private synthetic text.

Less is More: Adaptive Coverage for Synthetic Training Data Harnessing large-language models to generate private synthetic text

Reference 27

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Observation 27e5dcaa-f6fc-487d-a0ad-857a1a8da66a · outbound

This paper cites ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.

Less is More: Adaptive Coverage for Synthetic Training Data ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Reference 28

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Observation 8aef0bdb-7a5e-4b00-a1f6-04fb1fbd1096 · outbound

This paper cites Gecko: Versatile Text Embeddings Distilled from Large Language Models.

Less is More: Adaptive Coverage for Synthetic Training Data Gecko: Versatile Text Embeddings Distilled from Large Language Models

Reference 29

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Observation fcbc8854-dad3-417b-8633-c45af8b1cde5 · outbound

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

Less is More: Adaptive Coverage for Synthetic Training Data Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-16T11:51:08.066294Z

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Observation 56efb995-3ead-4f7c-9d6d-843d381b89d5 · outbound

This paper cites Best practices and lessons learned on synthetic data.

Less is More: Adaptive Coverage for Synthetic Training Data Best practices and lessons learned on synthetic data

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:51:08.653155Z

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Observation 85cb27ac-bc12-4aa4-ba5f-1475661b7ced · outbound

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

Less is More: Adaptive Coverage for Synthetic Training Data RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 32

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Observation 72e3640a-99cc-4244-9384-b3c27f2a477b · outbound

This paper cites Crossner: Evaluating cross-domain named entity recognition.

Less is More: Adaptive Coverage for Synthetic Training Data Crossner: Evaluating cross-domain named entity recognition

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:51:08.643372Z

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Observation 0df133c0-b627-4e3e-a31b-c5d7a15dd2a5 · outbound

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

Less is More: Adaptive Coverage for Synthetic Training Data On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-16T11:51:08.083171Z

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Observation 6c09618b-ff0e-4201-b6f6-6dc0bce9380b · outbound

This paper cites D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data Pruning.

Less is More: Adaptive Coverage for Synthetic Training Data D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data Pruning

Reference 35

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Observation 669aee43-6090-41af-adad-b006da5ba596 · outbound

This paper cites Generating training data with language models: Towards zero-shot language understanding.Advances in Neural Information Processing Systems 35 (2022), 462–477.

Less is More: Adaptive Coverage for Synthetic Training Data Generating training data with language models: Towards zero-shot language understanding.Advances in Neural Information Processing Systems 35 (2022), 462–477

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:51:08.632797Z

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Observation 41136fc9-8b1e-41f8-896a-721d62b72302 · outbound

This paper cites Adversarial Training Methods for Semi-Supervised Text Classification.

Less is More: Adaptive Coverage for Synthetic Training Data Adversarial Training Methods for Semi-Supervised Text Classification

Reference 37

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Observation 8bd8a733-d572-46af-bfc7-45b11a7119ce · outbound

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Less is More: Adaptive Coverage for Synthetic Training Data Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:51:08.622714Z

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Observation d9b190b5-6c10-4934-a55d-a5cca7572fc1 · outbound

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Less is More: Adaptive Coverage for Synthetic Training Data Unresolved cited work

Reference 39

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Observation 1ec5d887-7e83-4b0e-97e3-5a99cdcfebcd · outbound

This paper cites Scaling Language Models: Methods, Analysis & Insights from Training Gopher.

Less is More: Adaptive Coverage for Synthetic Training Data Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Reference 40

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Observation c68c4e5b-9d30-4ebd-a0b0-e49d91edb7e3 · outbound

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Less is More: Adaptive Coverage for Synthetic Training Data Unresolved cited work

Reference 41

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Observation c0facdb5-31c6-4bfd-ab7f-3a57ff010f28 · outbound

This paper cites C., Yates, A., and de Rijke, M.

Less is More: Adaptive Coverage for Synthetic Training Data C., Yates, A., and de Rijke, M

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:51:08.592315Z

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Observation 8f0aa3c1-cbd5-41ca-be46-58a8d2e5bb78 · outbound

This paper cites Data augmentation for intent classification with off-the-shelf large language models.

Less is More: Adaptive Coverage for Synthetic Training Data Data augmentation for intent classification with off-the-shelf large language models

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:51:08.581849Z

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Observation 3c4dee17-4f95-445a-8f4e-5d330bc06f7a · outbound

This paper cites Data sampling using locality sensitive hashing for large scale graph learning.

Less is More: Adaptive Coverage for Synthetic Training Data Data sampling using locality sensitive hashing for large scale graph learning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:51:08.571528Z

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Observation 57d61d7c-fe83-4a21-8c0d-7dce603f6c5b · outbound

This paper cites D., Agar w al, R., Anand, A., Patil, P., Garcia, X., Liu, P.

Less is More: Adaptive Coverage for Synthetic Training Data D., Agar w al, R., Anand, A., Patil, P., Garcia, X., Liu, P

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:51:08.560940Z

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Observation b77755a1-f023-4b12-b153-6053fbd5e54e · outbound

This paper cites D., Ng, A.

Less is More: Adaptive Coverage for Synthetic Training Data D., Ng, A

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:51:08.550744Z

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Observation 06faf962-7049-49a0-bc6f-ba494fad6058 · outbound

This paper cites Beyond neural scaling laws: beating power law scaling via data pruning.Advances in Neural Information Processing Systems 35 (2022), 19523–19536.

Less is More: Adaptive Coverage for Synthetic Training Data Beyond neural scaling laws: beating power law scaling via data pruning.Advances in Neural Information Processing Systems 35 (2022), 19523–19536

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:51:08.540406Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a96c1366-ad2d-450e-b0ad-0d3cfd38ba9e · outbound

This paper cites A., and Choi, Y.

Less is More: Adaptive Coverage for Synthetic Training Data A., and Choi, Y

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:51:08.527042Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a5659459-1dec-4c59-a7a7-8baf72d08f6f · outbound

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

Less is More: Adaptive Coverage for Synthetic Training Data Does Synthetic Data Generation of LLMs Help Clinical Text Mining?

Reference 49

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Observation ffc2d71d-a451-4a42-adb6-e97f9494d257 · outbound

This paper cites Galactica: A Large Language Model for Science.

Less is More: Adaptive Coverage for Synthetic Training Data Galactica: A Large Language Model for Science

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-16T11:51:08.142767Z

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Observation bf97eb36-61a2-4330-a309-5b80212aba71 · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Less is More: Adaptive Coverage for Synthetic Training Data Gemma: Open Models Based on Gemini Research and Technology

Reference 51

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Observation 62f3b55a-298d-487e-b64e-370fcb4a8bc5 · outbound

This paper cites An Empirical Study of Example Forgetting during Deep Neural Network Learning.

Less is More: Adaptive Coverage for Synthetic Training Data An Empirical Study of Example Forgetting during Deep Neural Network Learning

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-16T11:51:08.150941Z

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Observation bc3956b4-e7b6-432f-9376-9648279bdcbc · outbound

This paper cites an unresolved cited work.

Less is More: Adaptive Coverage for Synthetic Training Data Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:51:08.515861Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ae77f300-536b-4e93-8141-8eb35c251e3a · outbound

This paper cites EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks.

Less is More: Adaptive Coverage for Synthetic Training Data EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-16T11:51:08.158794Z

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Observation 95e5bd59-8602-4289-8c55-3bb83c2a223c · outbound

This paper cites Moderate coreset: A universal method of data selection for real-world data-efficient deep learning.

Less is More: Adaptive Coverage for Synthetic Training Data Moderate coreset: A universal method of data selection for real-world data-efficient deep learning

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:51:08.504293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f6f887e8-bde1-4530-aa0c-4178b7753cb1 · outbound

This paper cites Zerogen: Efficient zero-shot learning via dataset generation.

Less is More: Adaptive Coverage for Synthetic Training Data Zerogen: Efficient zero-shot learning via dataset generation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:51:08.492913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2a314016-d262-42a1-bb2a-1936e22a57d7 · outbound

This paper cites Coverage-centric coreset selection for high pruning rates.

Less is More: Adaptive Coverage for Synthetic Training Data Coverage-centric coreset selection for high pruning rates

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:51:08.481976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9594c818-2160-41dd-a8c0-67582fb15c76 · outbound

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

Less is More: Adaptive Coverage for Synthetic Training Data Texygen: A benchmarking platform for text generation models

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:51:08.470708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers