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 | 953* | 2021 |
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 | 843 | 2023 |
A unified game-theoretic approach to multiagent reinforcement learning M Lanctot, V Zambaldi, A Gruslys, A Lazaridou, K Tuyls, J Pérolat, D Silver, ... Advances in neural information processing systems 30, 2017 | 732 | 2017 |
Social influence as intrinsic motivation for multi-agent deep reinforcement learning N Jaques, A Lazaridou, E Hughes, C Gulcehre, P Ortega, DJ Strouse, ... International conference on machine learning, 3040-3049, 2019 | 522* | 2019 |
Multi-agent cooperation and the emergence of (natural) language A Lazaridou, A Peysakhovich, M Baroni arXiv preprint arXiv:1612.07182, 2016 | 502 | 2016 |
Improving zero-shot learning by mitigating the hubness problem G Dinu, A Lazaridou, M Baroni ICLR, Workshop Track, 2015 | 449 | 2015 |
The LAMBADA dataset: Word prediction requiring a broad discourse context D Paperno, G Kruszewski, A Lazaridou, QN Pham, R Bernardi, S Pezzelle, ... arXiv preprint arXiv:1606.06031, 2016 | 425 | 2016 |
Experience grounds language Y Bisk, A Holtzman, J Thomason, J Andreas, Y Bengio, J Chai, M Lapata, ... arXiv preprint arXiv:2004.10151, 2020 | 379 | 2020 |
Combining Language and Vision with a Multimodal Skip-gram Model A Lazaridou, NT Pham, M Baroni NAACL, 2015 | 350 | 2015 |
Hubness and Pollution: Delving into Cross-Space Mapping for Zero-Shot Learning A Lazaridou, G Dinu, M Baroni Proceedings of ACL 1, 270--280, 2015 | 257 | 2015 |
Emergence of linguistic communication from referential games with symbolic and pixel input A Lazaridou, KM Hermann, K Tuyls, S Clark arXiv preprint arXiv:1804.03984, 2018 | 240 | 2018 |
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 | 216* | 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 | 211* | 2021 |
Emergent communication through negotiation K Cao, A Lazaridou, M Lanctot, JZ Leibo, K Tuyls, S Clark arXiv preprint arXiv:1804.03980, 2018 | 187 | 2018 |
Emergent multi-agent communication in the deep learning era A Lazaridou, M Baroni arXiv preprint arXiv:2006.02419, 2020 | 178 | 2020 |
Internet-augmented language models through few-shot prompting for open-domain question answering A Lazaridou, E Gribovskaya, W Stokowiec, N Grigorev arXiv preprint arXiv:2203.05115, 2022 | 155 | 2022 |
Is this a wampimuk? cross-modal mapping between distributional semantics and the visual world A Lazaridou, E Bruni, M Baroni Proceedings of the 52nd Annual Meeting of the Association for Computational …, 2014 | 152 | 2014 |
Compositional-ly Derived Representations of Morphologically Complex Words in Distributional Semantics A Lazaridou, M Marelli, R Zamparelli, M Baroni Proceedings of ACL, Sofia, Bulgaria, 2013 | 144 | 2013 |
The repeval 2017 shared task: Multi-genre natural language inference with sentence representations N Nangia, A Williams, A Lazaridou, SR Bowman arXiv preprint arXiv:1707.08172, 2017 | 109 | 2017 |
Compositional obverter communication learning from raw visual input E Choi, A Lazaridou, N De Freitas arXiv preprint arXiv:1804.02341, 2018 | 103* | 2018 |