| Complex embeddings for simple link prediction T Trouillon, J Welbl, S Riedel, É Gaussier, G Bouchard International conference on machine learning, 2071-2080, 2016 | 2675 | 2016 |
| Constructing datasets for multi-hop reading comprehension across documents J Welbl, P Stenetorp, S Riedel Transactions of the Association for Computational Linguistics 6, 287-302, 2018 | 474 | 2018 |
| Scaling language models: Methods, analysis & insights from training gopher JW Rae, S Borgeaud, T Cai, K Millican, J Hoffmann, F Song, J Aslanides, ... arXiv preprint arXiv:2112.11446, 2021 | 468 | 2021 |
| Competition-level code generation with alphacode Y Li, D Choi, J Chung, N Kushman, J Schrittwieser, R Leblond, T Eccles, ... Science 378 (6624), 1092-1097, 2022 | 341 | 2022 |
| Knowledge graph completion via complex tensor factorization T Trouillon, CR Dance, J Welbl, S Riedel, É Gaussier, G Bouchard arXiv preprint arXiv:1702.06879, 2017 | 272 | 2017 |
| Training compute-optimal large language models J Hoffmann, S Borgeaud, A Mensch, E Buchatskaya, T Cai, E Rutherford, ... arXiv preprint arXiv:2203.15556, 2022 | 157 | 2022 |
| Crowdsourcing multiple choice science questions J Welbl, NF Liu, M Gardner arXiv preprint arXiv:1707.06209, 2017 | 153 | 2017 |
| Achieving verified robustness to symbol substitutions via interval bound propagation PS Huang, R Stanforth, J Welbl, C Dyer, D Yogatama, S Gowal, ... arXiv preprint arXiv:1909.01492, 2019 | 152 | 2019 |
| Frustratingly short attention spans in neural language modeling M Daniluk, T Rocktäschel, J Welbl, S Riedel arXiv preprint arXiv:1702.04521, 2017 | 127 | 2017 |
| Reducing sentiment bias in language models via counterfactual evaluation PS Huang, H Zhang, R Jiang, R Stanforth, J Welbl, J Rae, V Maini, ... arXiv preprint arXiv:1911.03064, 2019 | 124 | 2019 |
| Beat the AI: Investigating adversarial human annotation for reading comprehension M Bartolo, A Roberts, J Welbl, S Riedel, P Stenetorp Transactions of the Association for Computational Linguistics 8, 662-678, 2020 | 117 | 2020 |
| Ucl machine reading group: Four factor framework for fact finding (hexaf) T Yoneda, J Mitchell, J Welbl, P Stenetorp, S Riedel Proceedings of the First Workshop on Fact Extraction and VERification (FEVER …, 2018 | 110 | 2018 |
| Challenges in detoxifying language models J Welbl, A Glaese, J Uesato, S Dathathri, J Mellor, LA Hendricks, ... arXiv preprint arXiv:2109.07445, 2021 | 81 | 2021 |
| Neural random forests G Biau, E Scornet, J Welbl Sankhya A 81, 347-386, 2019 | 79 | 2019 |
| Cyprien de Masson d’Autume JW Rae, S Borgeaud, T Cai, K Millican, J Hoffmann, F Song, J Aslanides, ... | 58 | 2021 |
| Making sense of sensory input R Evans, J Hernández-Orallo, J Welbl, P Kohli, M Sergot Artificial Intelligence 293, 103438, 2021 | 51 | 2021 |
| Casting random forests as artificial neural networks (and profiting from it) J Welbl German Conference on Pattern Recognition, 765-771, 2014 | 49 | 2014 |
| Neural random forests G Biau, E Scornet, J Welbl arXiv preprint arXiv:1604.07143, 2016 | 43 | 2016 |
| Characteristics of harmful text: Towards rigorous benchmarking of language models M Rauh, J Mellor, J Uesato, PS Huang, J Welbl, L Weidinger, S Dathathri, ... Advances in Neural Information Processing Systems 35, 24720-24739, 2022 | 16 | 2022 |
| An empirical analysis of compute-optimal large language model training J Hoffmann, S Borgeaud, A Mensch, E Buchatskaya, T Cai, E Rutherford, ... Advances in Neural Information Processing Systems 35, 30016-30030, 2022 | 15 | 2022 |
Sebastian RiedelHonorary Professor @ University College London, Researcher @ DeepMindVerified email at cs.ucl.ac.uk
