| Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks P Lewis, E Perez, A Piktus, F Petroni, V Karpukhin, N Goyal, H Küttler, ... NeurIPS 2020, 2020 | 3692 | 2020 |
| FiLM: Visual Reasoning with a General Conditioning Layer E Perez, F Strub, H De Vries, V Dumoulin, A Courville AAAI 2018, 2018 | 2092 | 2018 |
| Constitutional AI: Harmlessness from AI Feedback Y Bai, S Kadavath, S Kundu, A Askell, J Kernion, A Jones, A Chen, ... arXiv preprint arXiv:2212.08073, 2022 | 906 | 2022 |
| ELI5: Long Form Question Answering A Fan, Y Jernite*, E Perez*, D Grangier, J Weston, M Auli Association for Computational Linguistics (ACL) 2019, 2019 | 480 | 2019 |
| Red teaming language models with language models E Perez, S Huang, F Song, T Cai, R Ring, J Aslanides, A Glaese, ... EMNLP 2022, 2022 | 438 | 2022 |
| True Few-Shot Learning with Language Models E Perez, D Kiela, K Cho NeurIPS 2021, 2021 | 374 | 2021 |
| Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned D Ganguli, L Lovitt, J Kernion, A Askell, Y Bai, S Kadavath, B Mann, ... arXiv preprint arXiv:2209.07858, 2022 | 348 | 2022 |
| Supervised multimodal bitransformers for classifying images and text D Kiela, S Bhooshan, H Firooz, E Perez, D Testuggine arXiv preprint arXiv:1909.02950, 2019 | 252 | 2019 |
| Language models don't always say what they think: unfaithful explanations in chain-of-thought prompting M Turpin, J Michael, E Perez, S Bowman Advances in Neural Information Processing Systems 36, 2024 | 229 | 2024 |
| Feature-wise transformations V Dumoulin, E Perez, N Schucher, F Strub, H Vries, A Courville, Y Bengio Distill 3 (7), e11, 2018 | 204* | 2018 |
| Discovering language model behaviors with model-written evaluations E Perez, S Ringer, K Lukošiūtė, K Nguyen, E Chen, S Heiner, C Pettit, ... arXiv preprint arXiv:2212.09251, 2022 | 187 | 2022 |
| Unsupervised Question Decomposition for Question Answering E Perez, P Lewis, W Yih, K Cho, D Kiela EMNLP 2020, 2020 | 168 | 2020 |
| Pretraining language models with human preferences T Korbak, K Shi, A Chen, R Bhalerao, CL Buckley, J Phang, SR Bowman, ... ICML 2023, 2023 | 152 | 2023 |
| HoME: a Household Multimodal Environment S Brodeur, E Perez*, A Anand*, F Golemo*, L Celotti, F Strub, J Rouat, ... ICLR 2018 Workshop, 2017 | 134 | 2017 |
| Language models (mostly) know what they know S Kadavath, T Conerly, A Askell, T Henighan, D Drain, E Perez, ... arXiv preprint arXiv:2207.05221, 2022 | 130 | 2022 |
| The capacity for moral self-correction in large language models D Ganguli, A Askell, N Schiefer, TI Liao, K Lukošiūtė, A Chen, A Goldie, ... arXiv preprint arXiv:2302.07459, 2023 | 124 | 2023 |
| Towards understanding sycophancy in language models M Sharma, M Tong, T Korbak, D Duvenaud, A Askell, SR Bowman, ... ICLR, 2023 | 92 | 2023 |
| Studying large language model generalization with influence functions R Grosse, J Bae, C Anil, N Elhage, A Tamkin, A Tajdini, B Steiner, D Li, ... arXiv preprint arXiv:2308.03296, 2023 | 80 | 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 | 78 | 2023 |
| Measuring progress on scalable oversight for large language models SR Bowman, J Hyun, E Perez, E Chen, C Pettit, S Heiner, K Lukošiūtė, ... arXiv preprint arXiv:2211.03540, 2022 | 73 | 2022 |
Douwe KielaContextual AI, Stanford UniversityVerified email at stanford.edu
