Lazaridou, Angeliki

Gemini: A Family of Highly Capable Multimodal Models

Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 990

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 306

A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

Lanctot, M., Zambaldi, V., Gruslys, A., Lazaridou, A., Tuyls, K., Perolat, J., Silver, D., & Graepel, T. (2017) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 295

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Rae, J. W., Borgeaud, S., Cai, T., Millican, K., Hoffmann, J., Song, F., Aslanides, J., Henderson, S., Ring, R., Young, S., et al. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 235

Multi-Agent Cooperation and the Emergence of (Natural) Language

Lazaridou, A., Peysakhovich, A., & Baroni, M. (2016) · arXiv

Language and cultural evolutionSpeech and dialogue systems

Cited by 231

Mind the Gap: Assessing Temporal Generalization in Neural Language Models

Lazaridou, A., Kuncoro, A., Gribovskaya, E., Agrawal, D., Liska, A., Terzi, T., Gimenez, M., d'Autume, C. D. M., Kocisky, T., Ruder, S., et al. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 90

The LAMBADA dataset: Word prediction requiring a broad discourse context

Paperno, D., Kruszewski, G., Lazaridou, A., Pham, N. Q., Bernardi, R., Pezzelle, S., Baroni, M., Boleda, G., & Fernandez, R. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Natural Language Processing TechniquesTopic Modeling

Cited by 87

Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input

Lazaridou, A., Hermann, K. M., Tuyls, K., & Clark, S. (2018) · arXiv

Language and cultural evolutionEvolutionary Algorithms and Applications

Cited by 86

Multimodal word meaning induction from minimal exposure to natural text

Lazaridou, A., Marelli, M., & Baroni, M. (2017) · Cognitive Science

Categorization, perception, and languageReading and Literacy Development

Cited by 80

Internet-augmented language models through few-shot prompting for open-domain question answering

Lazaridou, A., Gribovskaya, E., Stokowiec, W., & Grigorev, N. (2022) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 64

Biases for Emergent Communication in Multi-agent Reinforcement Learning

Eccles, T., Bachrach, Y., Lever, G., Lazaridou, A., & Graepel, T. (2019) · arXiv

Reinforcement Learning in RoboticsLanguage and cultural evolution

Cited by 42

StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models

Liška, A., Kočiský, T., Gribovskaya, E., Terzi, T., Sezener, E., Agrawal, D., d'Autume, C. D. M., Scholtes, T., Zaheer, M., Young, S., et al. (2022) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 19

Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries

Vodrahalli, K., Ontanon, S., Tripuraneni, N., Xu, K., Jain, S., Shivanna, R., Hui, J., Dikkala, N., Kazemi, M., Fatemi, B., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0