| 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 | 1132* | 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 | 1063 | 2021 |
| Episodic memory in lifelong language learning C de Masson D'Autume, S Ruder, L Kong, D Yogatama Advances in Neural Information Processing Systems 32, 2019 | 231 | 2019 |
| Learning and evaluating general linguistic intelligence D Yogatama, CM d'Autume, J Connor, T Kocisky, M Chrzanowski, L Kong, ... arXiv preprint arXiv:1901.11373, 2019 | 206* | 2019 |
| A mutual information maximization perspective of language representation learning L Kong, CM d'Autume, W Ling, L Yu, Z Dai, D Yogatama arXiv preprint arXiv:1910.08350, 2019 | 160* | 2019 |
| Mind the gap: Assessing temporal generalization in neural language models A Lazaridou, A Kuncoro, E Gribovskaya, D Agrawal, A Liska, T Terzi, ... Advances in Neural Information Processing Systems 34, 29348-29363, 2021 | 155* | 2021 |
| Adaptive semiparametric language models D Yogatama, C de Masson d’Autume, L Kong Transactions of the Association for Computational Linguistics 9, 362-373, 2021 | 106 | 2021 |
| Psychlab: a psychology laboratory for deep reinforcement learning agents JZ Leibo, CM d'Autume, D Zoran, D Amos, C Beattie, K Anderson, ... arXiv preprint arXiv:1801.08116, 2018 | 92* | 2018 |
| Training language gans from scratch C de Masson d'Autume, S Mohamed, M Rosca, J Rae Advances in Neural Information Processing Systems 32, 2019 | 85 | 2019 |
| A systematic investigation of commonsense knowledge in large language models XL Li, A Kuncoro, J Hoffmann, CM d'Autume, P Blunsom, A Nematzadeh arXiv preprint arXiv:2111.00607, 2021 | 54 | 2021 |
| Pitfalls of static language modelling A Lazaridou, A Kuncoro, E Gribovskaya, D Agrawal, A Liska, T Terzi, ... arXiv preprint arXiv:2102.01951, 2021 | 53* | 2021 |
| Streamingqa: A benchmark for adaptation to new knowledge over time in question answering models A Liska, T Kocisky, E Gribovskaya, T Terzi, E Sezener, D Agrawal, ... International Conference on Machine Learning, 13604-13622, 2022 | 48 | 2022 |
| Scaling Language Models: Methods JW Rae, S Borgeaud, T Cai, K Millican, J Hoffmann, F Song, J Aslanides, ... Analysis & Insights from Training Gopher. arXiv, 2021 | 26 | 2021 |
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| Vibe-Eval: A hard evaluation suite for measuring progress of multimodal language models P Padlewski, M Bain, M Henderson, Z Zhu, N Relan, H Pham, D Ong, ... arXiv preprint arXiv:2405.02287, 2024 | 12 | 2024 |
| Do language models learn commonsense knowledge XL Li, CMA Adhiguna Kuncoro, P Blunsom, A Nematzadeh arXiv preprint arXiv:2111.00607, 2021 | 5 | 2021 |
| Sentence encoding with tree-constrained relation networks L Yu, CM d'Autume, C Dyer, P Blunsom, L Kong, W Ling arXiv preprint arXiv:1811.10475, 2018 | 4 | 2018 |
| A systematic investigation of commonsense understanding in large language models XL Li, A Kuncoro, CM d’Autume, P Blunsom, A Nematzadeh CoRR, abs/2111.00607 1, 2021 | 3 | 2021 |
| Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models A Ormazabal, C Zheng, CM d'Autume, D Yogatama, D Fu, D Ong, E Chen, ... arXiv preprint arXiv:2404.12387, 2024 | 1 | 2024 |
| Computer code generation from task descriptions using neural networks Y Li, DH Choi, J Chung, NA Kushman, J Schrittwieser, R Leblond, ... US Patent App. 18/105,211, 2023 | | 2023 |
