I’m a Member of Technical Staff at Anthropic working on aligning language models with human preferences. Previously, I was a PhD student at the University of Sussex with Chris Buckley and Anil Seth focusing on RL from human feedback (RLHF) and spent time as a visiting researcher at NYU working with Ethan Perez, Sam Bowman and Kyunghyun Cho. I also interned at Naver Labs Europe and FAR AI. Before that, I studied cognitive science, philosophy and physics at the University of Warsaw and worked on compositional generalisation and emergent communication with Joanna Rączaszek-Leonardi and Piotr Miłoś, and on Bayesian accounts of self-organisation with Marcin Miłkowski.

Highlighted papers

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  Pretraining Language Models with Human Preferences

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

  ICML 2023 (oral)

  Code

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  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

  Code

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  Compositional preference models for aligning LMs

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

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

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

  Code

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  Taken out of context: On measuring situational awareness in LLMs

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

  Code

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  Training Language Models with Language Feedback at Scale

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

  Code

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  Improving Code Generation by Training with Natural Language Feedback

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

  Code

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  Aligning Language Models with Preferences through f-divergence Minimization

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

  ICML 2023

  Code

  ArXiv

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  On reinforcement learning and distribution matching for fine-tuning language models with no catastrophic forgetting

  Tomasz Korbak, Hady Elsahar, Germán Kruszewski, Marc Dymetman

  NeurIPS 2022 (oral)

  Code

  ArXiv

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  Controlling conditional language models without catastrophic forgetting

  Tomasz Korbak, Hady Elsahar, Germán Kruszewski, Marc Dymetman

  ICML 2022

  Code

  ArXiv

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  RL with KL penalties is better viewed as Bayesian inference

  Tomasz Korbak, Ethan Perez, Christopher Buckley

  Findings of EMNLP 2022

  ArXiv

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  Catalytic role of noise and necessity of inductive biases in emergence of compositional communication

  Łukasz Kuciński, Tomasz Korbak, Paweł Kołodziej, Piotr Miłoś

  NeurIPS 2021

  arXiv

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  Energy-based models for code generation under compilability constraints

  Tomasz Korbak, Hady Elsahar, Marc Dymetman, Germán Kruszewski

  NLP4Programming workshop, ACL 2021

  arXiv

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  Measuring non-trivial compositionality in emergent communication

  Tomasz Korbak, Julian Zubek, Joanna Rączaszek-Leonardi

  Emergent communication workshop, NeurIPS 2020

  Code

  arXiv code

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  Developmentally motivated emergence of compositional communication via template transfer

  Tomasz Korbak, Julian Zubek, Łukasz Kuciński, Piotr Miłoś, Joanna Rączaszek-Leonardi

  Emergent communication workshop, NeurIPS 2019

  Code

  arXiv code