Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 507 | 2023 |
Emergent communication through negotiation K Cao, A Lazaridou, M Lanctot, JZ Leibo, K Tuyls, S Clark arXiv preprint arXiv:1804.03980, 2018 | 178 | 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 | 124* | 2021 |
A joint model for word embedding and word morphology K Cao, M Rei arXiv preprint arXiv:1606.02601, 2016 | 114 | 2016 |
Latent variable dialogue models and their diversity K Cao, S Clark arXiv preprint arXiv:1702.05962, 2017 | 87 | 2017 |
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 | 83 | 2021 |
Control prefixes for parameter-efficient text generation J Clive, K Cao, M Rei arXiv preprint arXiv:2110.08329, 2021 | 62 | 2021 |
Factorising AMR generation through syntax K Cao, S Clark arXiv preprint arXiv:1804.07707, 2018 | 26 | 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 | 14 | 2022 |
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 13 | 2024 |
You should evaluate your language model on marginal likelihood over tokenisations K Cao, L Rimell arXiv preprint arXiv:2109.02550, 2021 | 11 | 2021 |
Towards coherent and consistent use of entities in narrative generation P Papalampidi, K Cao, T Kocisky International Conference on Machine Learning, 17278-17294, 2022 | 8 | 2022 |
Learning meaning representations for text generation with deep generative models K Cao | 8 | 2020 |
Modelling latent skills for multitask language generation K Cao, D Yogatama arXiv preprint arXiv:2002.09543, 2020 | 4 | 2020 |
What is the best recipe for character-level encoder-only modelling? K Cao arXiv preprint arXiv:2305.05461, 2023 | 2 | 2023 |
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 | | 2024 |
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 |