Korbak, Tomasz
12 papers on CoChatORCID 0000-0002-6258-2013
Most cited papers
- Verified
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Casper, S., Davies, X., Shi, C., Gilbert, T. K., Scheurer, J., Rando, J., Freedman, R., Korbak, T., Lindner, D., Freire, P., et al. (2023) · arXiv
Reinforcement Learning in RoboticsPsychiatry, Mental Health, Neuroscience
Cited by 101
- Verified
The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A"
Berglund, L., Tong, M., Kaufmann, M., Balesni, M., Stickland, A. C., Korbak, T., & Evans, O. (2023) · arXiv
Topic ModelingNatural Language Processing Techniques
Cited by 36
- Verified
Inverse Scaling: When Bigger Isn't Better
McKenzie, I. R., Lyzhov, A., Pieler, M., Parrish, A., Mueller, A., Prabhu, A., McLean, E., Kirtland, A., Ross, A., Liu, A., et al. (2023) · arXiv
Topic ModelingNatural Language Processing Techniques
Cited by 29
- Verified
Many-shot Jailbreaking
Anil, C., Durmus, E., Panickssery, N., Sharma, M., Benton, J., Kundu, S., Batson, J., Tong, M., Mu, J., Ford, D., et al. (2024) · Advances in Neural Information Processing Systems 37
Adversarial Robustness in Machine LearningSecurity and Verification in Computing
Cited by 25
- Verified
Catalytic Role Of Noise And Necessity Of Inductive Biases In The Emergence Of Compositional Communication
Kuciński, Ł., Korbak, T., Kołodziej, P., & Miłoś, P. (2021) · arXiv
Language and cultural evolutionGame Theory and Applications
Cited by 23
- Verified
Pretraining Language Models with Human Preferences
Korbak, T., Shi, K., Chen, A., Bhalerao, R., Buckley, C. L., Phang, J., Bowman, S. R., & Perez, E. (2023) · arXiv
Machine Learning and AlgorithmsInnovative Teaching and Learning Methods
Cited by 23
- Verified
Foundational Challenges in Assuring Alignment and Safety of Large Language Models
Anwar, U., Saparov, A., Rando, J., Paleka, D., Turpin, M., Hase, P., Lubana, E. S., Jenner, E., Casper, S., Sourbut, O., et al. (2024) · arXiv
Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning
Cited by 19
- Verified
Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data
Gerstgrasser, M., Schaeffer, R., Dey, A., Rafailov, R., Sleight, H., Hughes, J., Korbak, T., Agrawal, R., Pai, D., Gromov, A., et al. (2024) · arXiv
Generative Adversarial Networks and Image SynthesisTopic Modeling
Cited by 15
- Verified
Training Language Models with Language Feedback at Scale
Scheurer, J., Campos, J. A., Korbak, T., Chan, J. S., Chen, A., Cho, K., & Perez, E. (2023) · arXiv
Topic ModelingNatural Language Processing Techniques
Cited by 14
- Verified
RL with KL penalties is better viewed as Bayesian inference
Korbak, T., Perez, E., & Buckley, C. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022
Reinforcement Learning in RoboticsTopic Modeling
Cited by 8
- Verified
Taken out of context: On measuring situational awareness in LLMs
Berglund, L., Stickland, A. C., Balesni, M., Kaufmann, M., Tong, M., Korbak, T., Kokotajlo, D., & Evans, O. (2023) · arXiv
Topic ModelingAdversarial Robustness in Machine Learning
Cited by 8
- Verified
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Casper, S., Davies, X., Shi, C., Krendl Gilbert, T., Scheurer, J., Rando Ramirez, J., Freedman, R., Korbak, T., Lindner, D., Freire, P., et al. (2023) · ETH Zurich
Reinforcement Learning in RoboticsMachine Learning and Algorithms
Cited by 3
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