| Complex embeddings for simple link prediction T Trouillon, J Welbl, S Riedel, É Gaussier, G Bouchard International conference on machine learning, 2071-2080, 2016 | 4674 | 2016 |
| Training compute-optimal large language models J Hoffmann, S Borgeaud, A Mensch, E Buchatskaya, T Cai, E Rutherford, ... arXiv preprint arXiv:2203.15556, 2022 | 3394 | 2022 |
| 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 | 1971 | 2022 |
| 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 | 1538 | 2021 |
| Crowdsourcing multiple choice science questions J Welbl, NF Liu, M Gardner arXiv preprint arXiv:1707.06209, 2017 | 666 | 2017 |
| 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 | 648 | 2018 |
| Knowledge graph completion via complex tensor factorization T Trouillon, CR Dance, É Gaussier, J Welbl, S Riedel, G Bouchard Journal of Machine Learning Research 18 (130), 1-38, 2017 | 384 | 2017 |
| Challenges in detoxifying language models J Welbl, A Glaese, J Uesato, S Dathathri, J Mellor, LA Hendricks, ... arXiv preprint arXiv:2109.07445, 2021 | 316 | 2021 |
| Reducing sentiment bias in language models via counterfactual evaluation PS Huang, H Zhang, R Jiang, R Stanforth, J Welbl, J Rae, V Maini, ... Findings of the Association for Computational Linguistics: EMNLP 2020, 65-83, 2020 | 270 | 2020 |
| 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 | 232 | 2022 |
| 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 | 226 | 2020 |
| 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 | 207 | 2019 |
| Scalable watermarking for identifying large language model outputs S Dathathri, A See, S Ghaisas, PS Huang, R McAdam, J Welbl, V Bachani, ... Nature 634 (8035), 818-823, 2024 | 204 | 2024 |
| Frustratingly short attention spans in neural language modeling M Daniluk, T Rocktäschel, J Welbl, S Riedel arXiv preprint arXiv:1702.04521, 2017 | 166 | 2017 |
| Neural random forests G Biau, E Scornet, J Welbl Sankhya A 81 (2), 347-386, 2019 | 154 | 2019 |
| Collaboration between clinicians and vision–language models in radiology report generation R Tanno, DGT Barrett, A Sellergren, S Ghaisas, S Dathathri, A See, ... Nature Medicine 31 (2), 599-608, 2025 | 138 | 2025 |
| 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 | 131 | 2018 |
| Cyprien de Masson d’Autume JW Rae, S Borgeaud, T Cai, K Millican, J Hoffmann, F Song, J Aslanides, ... | 122 | 2021 |
| Training compute-optimal large language models (2022) J Hoffmann, S Borgeaud, A Mensch, E Buchatskaya, T Cai, E Rutherford, ... arXiv preprint arXiv:2203.15556, 2022 | 116 | 2022 |
| Cyprien de Masson d’Autume, Yujia Li, Tayfun Terzi, Vladimir Mikulik, Igor Babuschkin, Aidan Clark, Diego de Las Casas, Aurelia Guy, Chris Jones, James Bradbury, Matthew J JW Rae, S Borgeaud, T Cai, K Millican, J Hoffmann, HF Song, J Aslanides, ... Johnson, Blake A. Hechtman, Laura Weidinger, Iason Gabriel, William Isaac …, 2021 | 83 | 2021 |
Sebastian RiedelHonorary Professor @ University College London, Researcher @ DeepMindVerified email at cs.ucl.ac.uk
