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ORCIDTomasz Korbak
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2020 – today
- 2024
[j4]Angelica Chen, Jérémy Scheurer, Jon Ander Campos, Tomasz Korbak, Jun Shern Chan, Samuel R. Bowman, Kyunghyun Cho, Ethan Perez:
Learning from Natural Language Feedback. Trans. Mach. Learn. Res. 2024 (2024)- [c11]Lukas Berglund, Meg Tong, Maximilian Kaufmann, Mikita Balesni, Asa Cooper Stickland, Tomasz Korbak, Owain Evans:
The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A". ICLR 2024 - [c10]Dongyoung Go, Tomasz Korbak, Germán Kruszewski, Jos Rozen, Marc Dymetman:
Compositional Preference Models for Aligning LMs. ICLR 2024 - [c9]Mrinank Sharma, Meg Tong, Tomasz Korbak, David Duvenaud, Amanda Askell, Samuel R. Bowman, Esin Durmus, Zac Hatfield-Dodds, Scott R. Johnston, Shauna Kravec, Timothy Maxwell, Sam McCandlish, Kamal Ndousse, Oliver Rausch, Nicholas Schiefer, Da Yan, Miranda Zhang, Ethan Perez:
Towards Understanding Sycophancy in Language Models. ICLR 2024 - [i23]Matthias Gerstgrasser, Rylan Schaeffer, Apratim Dey, Rafael Rafailov, Henry Sleight, John Hughes, Tomasz Korbak, Rajashree Agrawal, Dhruv Pai, Andrey Gromov, Daniel A. Roberts, Diyi Yang, David L. Donoho, Sanmi Koyejo:
Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data. CoRR abs/2404.01413 (2024) - [i22]Usman Anwar, Abulhair Saparov, Javier Rando, Daniel Paleka, Miles Turpin, Peter Hase, Ekdeep Singh Lubana, Erik Jenner, Stephen Casper, Oliver Sourbut, Benjamin L. Edelman, Zhaowei Zhang, Mario Günther, Anton Korinek, José Hernández-Orallo, Lewis Hammond, Eric J. Bigelow, Alexander Pan, Lauro Langosco, Tomasz Korbak, Heidi Zhang, Ruiqi Zhong, Seán Ó hÉigeartaigh, Gabriel Recchia, Giulio Corsi, Alan Chan, Markus Anderljung, Lilian Edwards, Yoshua Bengio, Danqi Chen, Samuel Albanie, Tegan Maharaj, Jakob N. Foerster, Florian Tramèr, He He, Atoosa Kasirzadeh, Yejin Choi, David Krueger:
Foundational Challenges in Assuring Alignment and Safety of Large Language Models. CoRR abs/2404.09932 (2024) - [i21]Tomasz Korbak:
Aligning language models with human preferences. CoRR abs/2404.12150 (2024) - 2023
- [j3]Tomasz Korbak
:
Self-organisation, (M, R)-systems and enactive cognitive science. Adapt. Behav. 31(1): 35-49 (2023) - [j2]Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, Tony Tong Wang, Samuel Marks, Charbel-Raphaël Ségerie, Micah Carroll, Andi Peng, Phillip J. K. Christoffersen, Mehul Damani, Stewart Slocum, Usman Anwar, Anand Siththaranjan, Max Nadeau, Eric J. Michaud, Jacob Pfau, Dmitrii Krasheninnikov, Xin Chen, Lauro Langosco, Peter Hase, Erdem Biyik, Anca D. Dragan, David Krueger, Dorsa Sadigh, Dylan Hadfield-Menell:
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback. Trans. Mach. Learn. Res. 2023 (2023) - [j1]Ian R. McKenzie, Alexander Lyzhov, Michael Pieler, Alicia Parrish, Aaron Mueller, Ameya Prabhu, Euan McLean, Aaron Kirtland, Alexis Ross, Alisa Liu, Andrew Gritsevskiy, Daniel Wurgaft, Derik Kauffman, Gabriel Recchia, Jiacheng Liu, Joe Cavanagh, Max Weiss, Sicong Huang, The Floating Droid, Tom Tseng, Tomasz Korbak, Xudong Shen, Yuhui Zhang, Zhengping Zhou, Najoung Kim, Samuel R. Bowman, Ethan Perez:
Inverse Scaling: When Bigger Isn't Better. Trans. Mach. Learn. Res. 2023 (2023) - [c8]Dongyoung Go, Tomasz Korbak, Germán Kruszewski, Jos Rozen, Nahyeon Ryu, Marc Dymetman:
Aligning Language Models with Preferences through f-divergence Minimization. ICML 2023: 11546-11583 - [c7]Tomasz Korbak, Kejian Shi, Angelica Chen, Rasika Vinayak Bhalerao, Christopher L. Buckley, Jason Phang, Samuel R. Bowman, Ethan Perez:
Pretraining Language Models with Human Preferences. ICML 2023: 17506-17533 - [i20]Dongyoung Go, Tomasz Korbak, Germán Kruszewski, Jos Rozen, Nahyeon Ryu, Marc Dymetman:
Aligning Language Models with Preferences through f-divergence Minimization. CoRR abs/2302.08215 (2023) - [i19]Tomasz Korbak, Kejian Shi, Angelica Chen, Rasika Bhalerao, Christopher L. Buckley, Jason Phang, Samuel R. Bowman, Ethan Perez:
Pretraining Language Models with Human Preferences. CoRR abs/2302.08582 (2023) - [i18]Julian Zubek, Tomasz Korbak, Joanna Raczaszek-Leonardi:
Models of symbol emergence in communication: a conceptual review and a guide for avoiding local minima. CoRR abs/2303.04544 (2023) - [i17]Angelica Chen, Jérémy Scheurer, Tomasz Korbak, Jon Ander Campos, Jun Shern Chan, Samuel R. Bowman, Kyunghyun Cho, Ethan Perez:
Improving Code Generation by Training with Natural Language Feedback. CoRR abs/2303.16749 (2023) - [i16]Jérémy Scheurer, Jon Ander Campos, Tomasz Korbak, Jun Shern Chan, Angelica Chen, Kyunghyun Cho, Ethan Perez:
Training Language Models with Language Feedback at Scale. CoRR abs/2303.16755 (2023) - [i15]Ian R. McKenzie, Alexander Lyzhov, Michael Pieler, Alicia Parrish, Aaron Mueller, Ameya Prabhu, Euan McLean, Aaron Kirtland, Alexis Ross, Alisa Liu, Andrew Gritsevskiy, Daniel Wurgaft, Derik Kauffman, Gabriel Recchia, Jiacheng Liu, Joe Cavanagh, Max Weiss, Sicong Huang, The Floating Droid, Tom Tseng, Tomasz Korbak, Xudong Shen, Yuhui Zhang, Zhengping Zhou, Najoung Kim, Samuel R. Bowman, Ethan Perez:
Inverse Scaling: When Bigger Isn't Better. CoRR abs/2306.09479 (2023) - [i14]Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, Tony Tong Wang, Samuel Marks, Charbel-Raphaël Ségerie, Micah Carroll, Andi Peng, Phillip J. K. Christoffersen, Mehul Damani, Stewart Slocum, Usman Anwar, Anand Siththaranjan, Max Nadeau, Eric J. Michaud, Jacob Pfau, Dmitrii Krasheninnikov, Xin Chen, Lauro Langosco, Peter Hase, Erdem Biyik, Anca D. Dragan, David Krueger, Dorsa Sadigh, Dylan Hadfield-Menell:
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback. CoRR abs/2307.15217 (2023) - [i13]Lukas Berglund, Asa Cooper Stickland, Mikita Balesni, Maximilian Kaufmann, Meg Tong, Tomasz Korbak, Daniel Kokotajlo, Owain Evans:
Taken out of context: On measuring situational awareness in LLMs. CoRR abs/2309.00667 (2023) - [i12]Lukas Berglund, Meg Tong, Maximilian Kaufmann, Mikita Balesni, Asa Cooper Stickland, Tomasz Korbak, Owain Evans:
The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A". CoRR abs/2309.12288 (2023) - [i11]Dongyoung Go, Tomasz Korbak, Germán Kruszewski, Jos Rozen, Marc Dymetman:
Compositional preference models for aligning LMs. CoRR abs/2310.13011 (2023) - [i10]Mrinank Sharma, Meg Tong, Tomasz Korbak, David Duvenaud, Amanda Askell, Samuel R. Bowman, Newton Cheng, Esin Durmus, Zac Hatfield-Dodds, Scott R. Johnston, Shauna Kravec, Timothy Maxwell, Sam McCandlish, Kamal Ndousse, Oliver Rausch, Nicholas Schiefer, Da Yan, Miranda Zhang, Ethan Perez:
Towards Understanding Sycophancy in Language Models. CoRR abs/2310.13548 (2023) - 2022
- [c6]Tomasz Korbak, Ethan Perez, Christopher L. Buckley:
RL with KL penalties is better viewed as Bayesian inference. EMNLP (Findings) 2022: 1083-1091 - [c5]Tomasz Korbak, Hady Elsahar, Germán Kruszewski, Marc Dymetman:
Controlling Conditional Language Models without Catastrophic Forgetting. ICML 2022: 11499-11528 - [c4]Tomasz Korbak, Hady Elsahar, Germán Kruszewski, Marc Dymetman:
On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting. NeurIPS 2022 - [i9]Tomasz Korbak, Ethan Perez, Christopher L. Buckley:
RL with KL penalties is better viewed as Bayesian inference. CoRR abs/2205.11275 (2022) - [i8]Tomasz Korbak, Hady Elsahar, Germán Kruszewski, Marc Dymetman:
On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting. CoRR abs/2206.00761 (2022) - 2021
- [c3]Lukasz Kucinski, Tomasz Korbak, Pawel Kolodziej, Piotr Milos:
Catalytic Role Of Noise And Necessity Of Inductive Biases In The Emergence Of Compositional Communication. NeurIPS 2021: 23075-23088 - [i7]Tomasz Korbak, Hady Elsahar, Marc Dymetman, Germán Kruszewski:
Energy-Based Models for Code Generation under Compilability Constraints. CoRR abs/2106.04985 (2021) - [i6]Lukasz Kucinski, Tomasz Korbak, Pawel Kolodziej, Piotr Milos:
Catalytic Role Of Noise And Necessity Of Inductive Biases In The Emergence Of Compositional Communication. CoRR abs/2111.06464 (2021) - [i5]Tomasz Korbak, Hady Elsahar, Germán Kruszewski, Marc Dymetman:
Controlling Conditional Language Models with Distributional Policy Gradients. CoRR abs/2112.00791 (2021) - 2020
- [c2]Krzysztof Glówka, Michal Niklewski, Joanna Wiszowata, Tomasz Korbak, Joanna Raczaszek-Leonardi, Julian Zubek:
The Emergence of Action-grounded Compositional Communication. CogSci 2020 - [i4]Tomasz Korbak, Julian Zubek, Joanna Raczaszek-Leonardi:
Measuring non-trivial compositionality in emergent communication. CoRR abs/2010.15058 (2020)
2010 – 2019
- 2019
- [i3]Renard Korzeniowski, Rafal Rolczynski, Przemyslaw Sadownik, Tomasz Korbak, Marcin Mozejko:
Exploiting Unsupervised Pre-training and Automated Feature Engineering for Low-resource Hate Speech Detection in Polish. CoRR abs/1906.09325 (2019) - [i2]Tomasz Korbak, Julian Zubek, Lukasz Kucinski, Piotr Milos, Joanna Raczaszek-Leonardi:
Developmentally motivated emergence of compositional communication via template transfer. CoRR abs/1910.06079 (2019) - 2017
- [c1]Tomasz Korbak, Paulina Zak:
Fine-Tuning Tree-LSTM for Phrase-Level Sentiment Classification on a Polish Dependency Treebank. LCT 2017: 31-42 - [i1]Tomasz Korbak, Paulina Zak:
Fine-tuning Tree-LSTM for phrase-level sentiment classification on a Polish dependency treebank. Submission to PolEval task 2. CoRR abs/1711.01985 (2017)
Coauthor Index
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last updated on 2024-08-10 00:31 CEST by the dblp team
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