| GLUE: A multi-task benchmark and analysis platform for natural language understanding A Wang, A Singh, J Michael, F Hill, O Levy, SR Bowman arXiv preprint arXiv:1804.07461, 2018 | 5583 | 2018 |
| Superglue: A stickier benchmark for general-purpose language understanding systems A Wang, Y Pruksachatkun, N Nangia, A Singh, J Michael, F Hill, O Levy, ... Advances in neural information processing systems 32, 2019 | 1634 | 2019 |
| Simlex-999: Evaluating semantic models with (genuine) similarity estimation F Hill, R Reichart, A Korhonen Computational Linguistics 41 (4), 665-695, 2015 | 1487 | 2015 |
| The goldilocks principle: Reading children's books with explicit memory representations F Hill, A Bordes, S Chopra, J Weston arXiv preprint arXiv:1511.02301, 2015 | 674 | 2015 |
| The goldilocks principle: Reading children's books with explicit memory representations F Hill, A Bordes, S Chopra, J Weston arXiv preprint arXiv:1511.02301, 2015 | 668 | 2015 |
| Learning distributed representations of sentences from unlabelled data F Hill, K Cho, A Korhonen arXiv preprint arXiv:1602.03483, 2016 | 666 | 2016 |
| Multimodal few-shot learning with frozen language models M Tsimpoukelli, JL Menick, S Cabi, SM Eslami, O Vinyals, F Hill Advances in Neural Information Processing Systems 34, 200-212, 2021 | 419 | 2021 |
| Grounded language learning in a simulated 3d world KM Hermann, F Hill, S Green, F Wang, R Faulkner, H Soyer, D Szepesvari, ... arXiv preprint arXiv:1706.06551, 2017 | 315* | 2017 |
| Measuring abstract reasoning in neural networks A Santoro, F Hill, D Barrett, A Morcos, T Lillicrap International conference on machine learning, 4477-4486, 2018 | 300* | 2018 |
| Analysing mathematical reasoning abilities of neural models D Saxton, E Grefenstette, F Hill, P Kohli arXiv preprint arXiv:1904.01557, 2019 | 296 | 2019 |
| Simverb-3500: A large-scale evaluation set of verb similarity D Gerz, I Vuliĉ, F Hill, R Reichart, A Korhonen arXiv preprint arXiv:1608.00869, 2016 | 286 | 2016 |
| Neural arithmetic logic units A Trask, F Hill, SE Reed, J Rae, C Dyer, P Blunsom Advances in neural information processing systems 31, 2018 | 209 | 2018 |
| Learning to understand phrases by embedding the dictionary F Hill, K Cho, A Korhonen, Y Bengio Transactions of the Association for Computational Linguistics 4, 17-30, 2016 | 201 | 2016 |
| Specializing word embeddings for similarity or relatedness D Kiela, F Hill, S Clark Proceedings of the 2015 conference on empirical methods in natural language …, 2015 | 188 | 2015 |
| Learning to understand goal specifications by modelling reward D Bahdanau, F Hill, J Leike, E Hughes, A Hosseini, P Kohli, ... arXiv preprint arXiv:1806.01946, 2018 | 175* | 2018 |
| Can language models learn from explanations in context? AK Lampinen, I Dasgupta, SCY Chan, K Matthewson, MH Tessler, ... arXiv preprint arXiv:2204.02329, 2022 | 135 | 2022 |
| Placing language in an integrated understanding system: Next steps toward human-level performance in neural language models JL McClelland, F Hill, M Rudolph, J Baldridge, H Schütze Proceedings of the National Academy of Sciences 117 (42), 25966-25974, 2020 | 121* | 2020 |
| Environmental drivers of systematicity and generalization in a situated agent F Hill, A Lampinen, R Schneider, S Clark, M Botvinick, JL McClelland, ... arXiv preprint arXiv:1910.00571, 2019 | 117* | 2019 |
| Hyperlex: A large-scale evaluation of graded lexical entailment I Vuliĉ, D Gerz, D Kiela, F Hill, A Korhonen Computational Linguistics 43 (4), 781-835, 2017 | 115 | 2017 |
| Data distributional properties drive emergent in-context learning in transformers S Chan, A Santoro, A Lampinen, J Wang, A Singh, P Richemond, ... Advances in Neural Information Processing Systems 35, 18878-18891, 2022 | 107* | 2022 |
Anna KorhonenProfessor, Computer Science and Linguistics, University of CambridgeVerified email at cam.ac.uk
