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Large Language Models (LLMs) hold immense potential to generate synthetic data of high quality and utility, which has numerous applications from downstream model training to practical data utilisation.
The Curious Case of Neural Text Degeneration
Holtzman, A.; Buys, J.; Du, L.; Forbes, M.; and Choi, Y. 2020 · 1904
Earlier work this paper cites.
Neural Text Generation with Unlikelihood Training
Welleck, S.; Kulikov, I.; Roller, S.; Dinan, E.; Cho, K.; and Weston, J. 2019 · 1908
Earlier work this paper cites.
Mathematical Reasoning in Latent Space
Lee, D.; Szegedy, C.; Rabe, M. N.; Loos, S. M.; and Bansal, K. 2019 · 1909
Earlier work this paper cites.
Investigating Under and Overfitting in Wasserstein Generative Adversarial Networks
Adlam, B.; Weill, C.; and Kapoor, A. 2019 · 1910
Earlier work this paper cites.
Reliable Fidelity and Diversity Metrics for Generative Models
Naeem, M. F.; Oh, S. J.; Uh, Y.; Choi, Y.; and Yoo, J. 2020 · 2002
Earlier work this paper cites.
Data Augmentation using Pre-trained Transformer Models
Kumar, V.; Choudhary, A.; and Cho, E. 2021 · 2003
Earlier work this paper cites.
Compositional Visual Generation and Inference with Energy Based Models
Du, Y.; Li, S.; and Mordatch, I. 2020 · 2004
Earlier work this paper cites.
A Non-Parametric Test to Detect Data-Copying in Generative Models
Meehan, C.; Chaudhuri, K.; and Dasgupta, S. 2020 · 2004
Earlier work this paper cites.
CG-BERT: Conditional Text Generation with BERT for Generalized Few-shot Intent Detection
Xia, C.; Zhang, C.; Nguyen, H.; Zhang, J.; and Yu, P. 2020 · 2004
Earlier work this paper cites.
GeDi: Generative Discriminator Guided Sequence Generation
Krause, B.; Gotmare, A. D.; McCann, B.; Keskar, N. S.; Joty, S.; Socher, R.; and Rajani, N. F. 2020 · 2009
Earlier work this paper cites.
Generative Adversarial Networks
Goodfellow, I. J.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; and Bengio, Y. 2014 · 2014
Earlier work this paper cites.
Precision-recall-gain curves: PR analysis done right
Flach, P.; and Kull, M. 2015 · 2015
Earlier work this paper cites.
Deep learning
Goodfellow, I.; Bengio, Y.; and Courville, A. 2016 · 2016
Earlier work this paper cites.
A note on the evaluation of generative models
Theis, L.; van den Oord, A.; and Bethge, M. 2016 · 2016
Earlier work this paper cites.
Character-level Convolutional Networks for Text Classification
Zhang, X.; Zhao, J.; and LeCun, Y. 2016 · 2016
Earlier work this paper cites.
Data Augmentation for Low-Resource Neural Machine Translation
Fadaee, M.; Bisazza, A.; and Monz, C. 2017 · 2017
Earlier work this paper cites.
On the Quantitative Analysis of Decoder-Based Generative Models
Wu, Y.; Burda, Y.; Salakhutdinov, R.; and Grosse, R. 2017 · 2017
Earlier work this paper cites.
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Heusel, M.; Ramsauer, H.; Unterthiner, T.; Nessler, B.; and Hochreiter, S. 2018 · 2018
Earlier work this paper cites.
Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding
Hou, Y.; Liu, Y.; Che, W.; and Liu, T. 2018 · 2018
Earlier work this paper cites.
Assessing Generative Models via Precision and Recall
Sajjadi, M. S. M.; Bachem, O.; Lucic, M.; Bousquet, O.; and Gelly, S. 2018 · 2018
Earlier work this paper cites.
Data Augmentation for Spoken Language Understanding via Joint Variational Generation
Yoo, K. M.; Shin, Y.; and goo Lee, S. 2018 · 2018
Earlier work this paper cites.
mixup: Beyond Empirical Risk Minimization
Zhang, H.; Cisse, M.; Dauphin, Y. N.; and Lopez-Paz, D. 2018 · 2018
Earlier work this paper cites.
EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks
Wei, J.; and Zou, K. 2019 · 2019
Earlier work this paper cites.
Plug and Play Language Models: A Simple Approach to Controlled Text Generation
Dathathri, S.; Madotto, A.; Lan, J.; Hung, J.; Frank, E.; Molino, P.; Yosinski, J.; and Liu, R. 2020 · 2020
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Sequence-Level Mixed Sample Data Augmentation
Guo, D.; Kim, Y.; and Rush, A. 2020 · 2020
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Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation
Liu, R.; Xu, G.; Jia, C.; Ma, W.; Wang, L.; and Vosoughi, S. 2020 · 2020
Cited alongside, same era.
SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness
Ng, N.; Cho, K.; and Ghassemi, M. 2020 · 2020
Cited alongside, same era.
Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks
Sun, L.; Xia, C.; Yin, W.; Liang, T.; Yu, P.; and He, L. 2020 · 2020
Cited alongside, same era.
Classifiers are Better Experts for Controllable Text Generation
Sitdikov, A.; Balagansky, N.; Gavrilov, D.; and Markov, A. 2022 · 2022
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A Contrastive Framework for Neural Text Generation
Su, Y.; Lan, T.; Wang, Y.; Yogatama, D.; Kong, L.; and Collier, N. 2022 · 2022
Later among the works it cites.
Using Synthetic Data for Conversational Response Generation in Low-resource Settings
Tan, G. L.; Ty, A. P.; Ng, S.; Co, D. A.; Cruz, J. C. B.; and Cheng, C. 2022 · 2022
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Emergent Abilities of Large Language Models
Wei, J.; Tay, Y.; Bommasani, R.; Raffel, C.; Zoph, B.; Borgeaud, S.; Yogatama, D.; Bosma, M.; Zhou, D.; Metzler, D.; Chi, E. H.; Hashimoto, T.; Vinyals, O.; Liang, P.; Dean, J.; and Fedus, W. 2022 · 2022
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Feng, S. Y.; Gangal, V.; Wei, J.; Chandar, S.; Vosoughi, S.; Mitamura, T.; and Hovy, E. 2021 · 2021
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DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts
Liu, A.; Sap, M.; Lu, X.; Swayamdipta, S.; Bhagavatula, C.; Smith, N. A.; and Choi, Y. 2021 · 2021
Cited alongside, same era.
MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers
Pillutla, K.; Swayamdipta, S.; Zellers, R.; Thickstun, J.; Welleck, S.; Choi, Y.; and Harchaoui, Z. 2021 · 2021
Cited alongside, same era.
Latent Space Refinement for Deep Generative Models
Winterhalder, R.; Bellagente, M.; and Nachman, B. 2021 · 2021
Cited alongside, same era.
GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation
Yoo, K. M.; Park, D.; Kang, J.; Lee, S.-W.; and Park, W. 2021 · 2021
Cited alongside, same era.
SynthBio: A Case Study in Faster Curation of Text Datasets
Yuan, A.; Ippolito, D.; Nikolaev, V.; Callison-Burch, C.; Coenen, A.; and Gehrmann, S. 2021 · 2021
Cited alongside, same era.
Alaa, A. M.; van Breugel, B.; Saveliev, E.; and van der Schaar, M. 2022 · 2022
Cited alongside, same era.
Wu, X.; Gao, C.; Lin, M.; Zang, L.; Wang, Z.; and Hu, S. 2022 · 2022
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TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understanding
Zhang, L.; Yang, Z.; and Yang, D. 2022 · 2022
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How does negative prompt work?
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Art and the science of generative AI
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Semantic Compression With Large Language Models
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Synthetically generated text for supervised text analysis
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A Latent Space Theory for Emergent Abilities in Large Language Models
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The promise and peril of generative AI
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Machine Learning for Synthetic Data Generation: A Review
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Negative-prompt Inversion: Fast Image Inversion for Editing with Text-guided Diffusion Models
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Stay on topic with Classifier-Free Guidance
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Talking About Large Language Models
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Trusting Your Evidence: Hallucinate Less with Context-aware Decoding
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A Survey of Large Language Models
Zhao, W. X.; Zhou, K.; Li, J.; Tang, T.; Wang, X.; Hou, Y.; Min, Y.; Zhang, B.; Zhang, J.; Dong, Z.; Du, Y.; Yang, C.; Chen, Y.; Chen, Z.; Jiang, J.; Ren, R.; Li, Y.; Tang, X.; Liu, Z.; Liu, P.; Nie, J.-Y.; and Wen, J.-R. 2023 · 2023
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