| Open problems and fundamental limitations of reinforcement learning from human feedback S Casper, X Davies, C Shi, TK Gilbert, J Scheurer, J Rando, R Freedman, ... arXiv preprint arXiv:2307.15217, 2023 | 397 | 2023 |
| The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A" L Berglund, M Tong, M Kaufmann, M Balesni, AC Stickland, T Korbak, ... arXiv preprint arXiv:2309.12288, 2023 | 206* | 2023 |
| Pretraining language models with human preferences T Korbak, K Shi, A Chen, RV Bhalerao, C Buckley, J Phang, SR Bowman, ... International Conference on Machine Learning, 17506-17533, 2023 | 180 | 2023 |
| Towards understanding sycophancy in language models M Sharma, M Tong, T Korbak, D Duvenaud, A Askell, SR Bowman, ... arXiv preprint arXiv:2310.13548, 2023 | 147* | 2023 |
| Inverse scaling: When bigger isn't better IR McKenzie, A Lyzhov, M Pieler, A Parrish, A Mueller, A Prabhu, ... arXiv preprint arXiv:2306.09479, 2023 | 118* | 2023 |
| Training language models with language feedback at scale J Scheurer, JA Campos, T Korbak, JS Chan, A Chen, K Cho, E Perez arXiv preprint arXiv:2303.16755, 2023 | 97 | 2023 |
| Foundational challenges in assuring alignment and safety of large language models U Anwar, A Saparov, J Rando, D Paleka, M Turpin, P Hase, ES Lubana, ... arXiv preprint arXiv:2404.09932, 2024 | 84 | 2024 |
| Many-shot jailbreaking C Anil, E Durmus, N Rimsky, M Sharma, J Benton, S Kundu, J Batson, ... The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024 | 79* | 2024 |
| Aligning language models with preferences through f-divergence minimization D Go, T Korbak, G Kruszewski, J Rozen, N Ryu, M Dymetman arXiv preprint arXiv:2302.08215, 2023 | 61 | 2023 |
| Improving code generation by training with natural language feedback A Chen, J Scheurer, T Korbak, JA Campos, JS Chan, SR Bowman, K Cho, ... arXiv preprint arXiv:2303.16749, 2023 | 57 | 2023 |
| RL with KL penalties is better viewed as Bayesian inference T Korbak, E Perez, CL Buckley arXiv preprint arXiv:2205.11275, 2022 | 51 | 2022 |
| On reinforcement learning and distribution matching for fine-tuning language models with no catastrophic forgetting T Korbak, H Elsahar, G Kruszewski, M Dymetman Advances in Neural Information Processing Systems 35, 16203-16220, 2022 | 50 | 2022 |
| Taken out of context: On measuring situational awareness in LLMs L Berglund, AC Stickland, M Balesni, M Kaufmann, M Tong, T Korbak, ... arXiv preprint arXiv:2309.00667, 2023 | 49* | 2023 |
| Controlling conditional language models without catastrophic forgetting T Korbak, H Elsahar, G Kruszewski, M Dymetman International Conference on Machine Learning, 11499-11528, 2022 | 35 | 2022 |
| Computational enactivism under the free energy principle T Korbak Synthese 198 (3), 2743-2763, 2021 | 34 | 2021 |
| 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, ... arXiv preprint arXiv:2404.01413, 2024 | 28* | 2024 |
| Interaction history as a source of compositionality in emergent communication T Korbak, J Zubek, Ł Kuciński, P Miłoś, J Rączaszek-Leonardi Interaction Studies 22 (2), 212-243, 2021 | 21* | 2021 |
| Catalytic role of noise and necessity of inductive biases in the emergence of compositional communication Ł Kuciński, T Korbak, P Kołodziej, P Miłoś Advances in neural information processing systems 34, 23075-23088, 2021 | 18* | 2021 |
| Measuring non-trivial compositionality in emergent communication T Korbak, J Zubek, J Rączaszek-Leonardi arXiv preprint arXiv:2010.15058, 2020 | 12 | 2020 |
| Compositional preference models for aligning LMs D Go, T Korbak, G Kruszewski, J Rozen, M Dymetman arXiv preprint arXiv:2310.13011, 2023 | 10 | 2023 |
Ethan PerezAnthropic; New York UniversityVerified email at anthropic.com
