Tomasz Korbak

According to our database, Tomasz Korbak authored at least 29 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Learning from Natural Language Feedback.

Angelica Chen, Jérémy Scheurer, Jon Ander Campos, Tomasz Korbak, Jun Shern Chan, Samuel R. Bowman, Kyunghyun Cho, Ethan Perez

Trans. Mach. Learn. Res., 2024

Aligning language models with human preferences.

Tomasz Korbak

CoRR, 2024

Foundational Challenges in Assuring Alignment and Safety of Large Language Models.

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

CoRR, 2024

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

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

CoRR, 2024

Towards Understanding Sycophancy in Language Models.

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

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Compositional Preference Models for Aligning LMs.

Dongyoung Go, Tomasz Korbak, Germán Kruszewski, Jos Rozen, Marc Dymetman

Proceedings of the Twelfth International Conference on Learning Representations, 2024

The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A".

Lukas Berglund, Meg Tong, Maximilian Kaufmann, Mikita Balesni, Asa Cooper Stickland, Tomasz Korbak, Owain Evans

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Self-organisation, (M, R)-systems and enactive cognitive science.

Tomasz Korbak

Adapt. Behav., February, 2023

Inverse Scaling: When Bigger Isn't Better.

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

Trans. Mach. Learn. Res., 2023

Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback.

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

Trans. Mach. Learn. Res., 2023

Towards Understanding Sycophancy in Language Models.

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

CoRR, 2023

Taken out of context: On measuring situational awareness in LLMs.

Lukas Berglund, Asa Cooper Stickland, Mikita Balesni, Maximilian Kaufmann, Meg Tong, Tomasz Korbak, Daniel Kokotajlo, Owain Evans

CoRR, 2023

Training Language Models with Language Feedback at Scale.

Jérémy Scheurer, Jon Ander Campos, Tomasz Korbak, Jun Shern Chan, Angelica Chen, Kyunghyun Cho, Ethan Perez

CoRR, 2023

Improving Code Generation by Training with Natural Language Feedback.

Angelica Chen, Jérémy Scheurer, Tomasz Korbak, Jon Ander Campos, Jun Shern Chan, Samuel R. Bowman, Kyunghyun Cho, Ethan Perez

CoRR, 2023

Models of symbol emergence in communication: a conceptual review and a guide for avoiding local minima.

Julian Zubek, Tomasz Korbak, Joanna Raczaszek-Leonardi

CoRR, 2023

Pretraining Language Models with Human Preferences.

Tomasz Korbak, Kejian Shi, Angelica Chen, Rasika Vinayak Bhalerao, Christopher L. Buckley, Jason Phang, Samuel R. Bowman, Ethan Perez

Proceedings of the International Conference on Machine Learning, 2023

Aligning Language Models with Preferences through f-divergence Minimization.

Dongyoung Go, Tomasz Korbak, Germán Kruszewski, Jos Rozen, Nahyeon Ryu, Marc Dymetman

Proceedings of the International Conference on Machine Learning, 2023

2022

On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting.

Tomasz Korbak, Hady Elsahar, Germán Kruszewski, Marc Dymetman

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Controlling Conditional Language Models without Catastrophic Forgetting.

Tomasz Korbak, Hady Elsahar, Germán Kruszewski, Marc Dymetman

Proceedings of the International Conference on Machine Learning, 2022

RL with KL penalties is better viewed as Bayesian inference.

Tomasz Korbak, Ethan Perez, Christopher L. Buckley

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 2022

2021

Controlling Conditional Language Models with Distributional Policy Gradients.

Tomasz Korbak, Hady Elsahar, Germán Kruszewski, Marc Dymetman

CoRR, 2021

Energy-Based Models for Code Generation under Compilability Constraints.

Tomasz Korbak, Hady Elsahar, Marc Dymetman, Germán Kruszewski

CoRR, 2021

Catalytic Role Of Noise And Necessity Of Inductive Biases In The Emergence Of Compositional Communication.

Lukasz Kucinski, Tomasz Korbak, Pawel Kolodziej, Piotr Milos

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Measuring non-trivial compositionality in emergent communication.

Tomasz Korbak, Julian Zubek, Joanna Raczaszek-Leonardi

CoRR, 2020

The Emergence of Action-grounded Compositional Communication.

Krzysztof Glówka, Michal Niklewski, Joanna Wiszowata, Tomasz Korbak, Joanna Raczaszek-Leonardi, Julian Zubek

Proceedings of the 42th Annual Meeting of the Cognitive Science Society, 2020

2019

Developmentally motivated emergence of compositional communication via template transfer.

Tomasz Korbak, Julian Zubek, Lukasz Kucinski, Piotr Milos, Joanna Raczaszek-Leonardi

CoRR, 2019

Exploiting Unsupervised Pre-training and Automated Feature Engineering for Low-resource Hate Speech Detection in Polish.

Renard Korzeniowski, Rafal Rolczynski, Przemyslaw Sadownik, Tomasz Korbak, Marcin Mozejko

CoRR, 2019

2017

Fine-tuning Tree-LSTM for phrase-level sentiment classification on a Polish dependency treebank. Submission to PolEval task 2.

Tomasz Korbak, Paulina Zak

CoRR, 2017

Fine-Tuning Tree-LSTM for Phrase-Level Sentiment Classification on a Polish Dependency Treebank.

Tomasz Korbak, Paulina Zak

Proceedings of the Human Language Technology. Challenges for Computer Science and Linguistics, 2017