| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 3445 | 2023 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 1319 | 2024 |
| Emergent communication through negotiation K Cao, A Lazaridou, M Lanctot, JZ Leibo, K Tuyls, S Clark arXiv preprint arXiv:1804.03980, 2018 | 207 | 2018 |
| 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 | 183* | 2021 |
| A joint model for word embedding and word morphology K Cao, M Rei arXiv preprint arXiv:1606.02601, 2016 | 121 | 2016 |
| Game Plan: What AI can do for Football, and What Football can do for AI K Tuyls, S Omidshafiei, P Muller, Z Wang, J Connor, D Hennes, I Graham, ... Journal of Artificial Intelligence Research 71, 41-88, 2021 | 115 | 2021 |
| Latent variable dialogue models and their diversity K Cao, S Clark arXiv preprint arXiv:1702.05962, 2017 | 89 | 2017 |
| Control prefixes for parameter-efficient text generation J Clive, K Cao, M Rei arXiv preprint arXiv:2110.08329, 2021 | 69 | 2021 |
| Factorising AMR generation through syntax K Cao, S Clark arXiv preprint arXiv:1804.07707, 2018 | 27 | 2018 |
| Multiagent off-screen behavior prediction in football S Omidshafiei, D Hennes, M Garnelo, Z Wang, A Recasens, E Tarassov, ... Scientific reports 12 (1), 8638, 2022 | 26 | 2022 |
| You should evaluate your language model on marginal likelihood over tokenisations K Cao, L Rimell arXiv preprint arXiv:2109.02550, 2021 | 20 | 2021 |
| Learning meaning representations for text generation with deep generative models K Cao | 17 | 2020 |
| Unpacking tokenization: Evaluating text compression and its correlation with model performance O Goldman, A Caciularu, M Eyal, K Cao, I Szpektor, R Tsarfaty arXiv preprint arXiv:2403.06265, 2024 | 12 | 2024 |
| Towards coherent and consistent use of entities in narrative generation P Papalampidi, K Cao, T Kocisky International Conference on Machine Learning, 17278-17294, 2022 | 11 | 2022 |
| What is the best recipe for character-level encoder-only modelling? K Cao arXiv preprint arXiv:2305.05461, 2023 | 4 | 2023 |
| Modelling latent skills for multitask language generation K Cao, D Yogatama arXiv preprint arXiv:2002.09543, 2020 | 4 | 2020 |
| Dynamic entity representations for sequence generation KY Cao, T Kocisky, P Papalampidi US Patent App. 17/960,775, 2023 | | 2023 |
| Factorising K Cao, S Clark Proceedings of the 2019 Conference of the North, 2019 | | 2019 |
| Proceedings of the Third Workshop on Representation Learning for NLP I Augenstein, K Cao, H He, F Hill, S Gella, J Kiros, H Mei, D Misra Proceedings of the Third Workshop on Representation Learning for NLP, 2018 | | 2018 |
| CPGS First Year Report K Cao | | 2015 |
Stephen ClarkQuantinuumVerified email at cl.cam.ac.uk
