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Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data.

M. Gerstgrasser, R. Schaeffer, A. Dey, R. Rafailov, H. Sleight, J. Hughes, T. Korbak, R. Agrawal, D. Pai, A. Gromov, D. Roberts, D. Yang, D. Donoho, and S. Koyejo. CoRR, (2024)

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Tomasz Kupka

Ascheablagerungsprobleme bei der Mitverbrennung von alternativen Brennstoffen in SteinkohlekraftwerkenT. Kupka. TU Clausthal-Zellerfeld, (2009)

Tomasz Wocjan

Dynamics of DNA in nucleosomes and plasmids studied by Brownian dynamicsT. Wocjan. Uni Heidelberg, (2009)

Tomasz Mistrzyk

Tomasz Zyss

Tomasz Trela

Other publications of authors with the same name

On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting.T. Korbak, H. Elsahar, G. Kruszewski, and M. Dymetman. NeurIPS, (2022)Towards Understanding Sycophancy in Language Models.M. Sharma, M. Tong, T. Korbak, D. Duvenaud, A. Askell, S. Bowman, N. Cheng, E. Durmus, Z. Hatfield-Dodds, S. Johnston and 9 other author(s). CoRR, (2023)Catalytic Role Of Noise And Necessity Of Inductive Biases In The Emergence Of Compositional Communication.L. Kucinski, T. Korbak, P. Kolodziej, and P. Milos. NeurIPS, page 23075-23088. (2021)Inverse Scaling: When Bigger Isn't Better.I. McKenzie, A. Lyzhov, M. Pieler, A. Parrish, A. Mueller, A. Prabhu, E. McLean, A. Kirtland, A. Ross, A. Liu and 17 other author(s). Trans. Mach. Learn. Res., (2023)Foundational Challenges in Assuring Alignment and Safety of Large Language Models.U. Anwar, A. Saparov, J. Rando, D. Paleka, M. Turpin, P. Hase, E. Lubana, E. Jenner, S. Casper, O. Sourbut and 28 other author(s). CoRR, (2024)Aligning Language Models with Preferences through f-divergence Minimization.D. Go, T. Korbak, G. Kruszewski, J. Rozen, N. Ryu, and M. Dymetman. ICML, volume 202 of Proceedings of Machine Learning Research, page 11546-11583. PMLR, (2023)Fine-Tuning Tree-LSTM for Phrase-Level Sentiment Classification on a Polish Dependency Treebank.T. Korbak, and P. Zak. LCT, volume 12598 of Lecture Notes in Computer Science, page 31-42. Springer, (2017)Learning from Natural Language Feedback.A. Chen, J. Scheurer, J. Campos, T. Korbak, J. Chan, S. Bowman, K. Cho, and E. Perez. Trans. Mach. Learn. Res., (2024)Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback.S. Casper, X. Davies, C. Shi, T. Gilbert, J. Scheurer, J. Rando, R. Freedman, T. Korbak, D. Lindner, P. Freire and 22 other author(s). Trans. Mach. Learn. Res., (2023)The Reversal Curse: LLMs trained on Ä is B" fail to learn "B is A".L. Berglund, M. Tong, M. Kaufmann, M. Balesni, A. Stickland, T. Korbak, and O. Evans. ICLR, OpenReview.net, (2024)

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Disambiguation of "Korbak, Tomasz"

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Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data.

Please choose a person to relate this publication to

Tomasz Kupka

Tomasz Wocjan

Tomasz Mistrzyk

Tomasz Zyss

Tomasz Trela

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Korbak, Tomasz"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data.

Please choose a person to relate this publication to

Tomasz Kupka

Tomasz Wocjan

Tomasz Mistrzyk

Tomasz Zyss

Tomasz Trela

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data.