297 citations · 1.6k across the 27 of their papers we have counts for
32 papers
Long-Span Question-Answering: Automatic Question Generation and QA-System Ranking via Side-by-Side Evaluation
Bernd Bohnet, Kevin Swersky, Rosanne Liu +9
We explore the use of long-context capabilities in large language models to create synthetic reading comprehension data from entire books. Previous efforts to construct such datase…
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Gemini Team, Petko Georgiev, Ving Ian Lei +1132
In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over…
Towards Robust and Efficient Continual Language Learning
Adam Fisch, Amal Rannen-Triki, Razvan Pascanu +4
As the application space of language models continues to evolve, a natural question to ask is how we can quickly adapt models to new tasks. We approach this classic question from a…
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research
Jorg Bornschein, Alexandre Galashov, Ross Hemsley +17
A shared goal of several machine learning communities like continual learning, meta-learning and transfer learning, is to design algorithms and models that efficiently and robustly…
StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models
Adam Liška, Tomáš Kočiský, Elena Gribovskaya +11
Knowledge and language understanding of models evaluated through question answering (QA) has been usually studied on static snapshots of knowledge, like Wikipedia. However, our wor…
Internet-augmented language models through few-shot prompting for open-domain question answering
Angeliki Lazaridou, Elena Gribovskaya, Wojciech Stokowiec +1
In this work, we aim to capitalize on the unique few-shot capabilities of large-scale language models (LSLMs) to overcome some of their challenges with respect to grounding to fact…
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Jack W. Rae, Sebastian Borgeaud, Trevor Cai +77
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.…
Dynamic population-based meta-learning for multi-agent communication with natural language
Abhinav Gupta, Marc Lanctot, Angeliki Lazaridou
In this work, our goal is to train agents that can coordinate with seen, unseen as well as human partners in a multi-agent communication environment involving natural language. Pre…
Mind the Gap: Assessing Temporal Generalization in Neural Language Models
Angeliki Lazaridou, Adhiguna Kuncoro, Elena Gribovskaya +11
Our world is open-ended, non-stationary, and constantly evolving; thus what we talk about and how we talk about it change over time. This inherent dynamic nature of language contra…
Emergent Communication under Competition
Michael Noukhovitch, Travis LaCroix, Angeliki Lazaridou +1
The literature in modern machine learning has only negative results for learning to communicate between competitive agents using standard RL. We introduce a modified sender-receive…
Negotiating Team Formation Using Deep Reinforcement Learning
Yoram Bachrach, Richard Everett, Edward Hughes +6
When autonomous agents interact in the same environment, they must often cooperate to achieve their goals. One way for agents to cooperate effectively is to form a team, make a bin…
Emergent Multi-Agent Communication in the Deep Learning Era
Angeliki Lazaridou, Marco Baroni
The ability to cooperate through language is a defining feature of humans. As the perceptual, motory and planning capabilities of deep artificial networks increase, researchers are…
Multi-agent Communication meets Natural Language: Synergies between Functional and Structural Language Learning
Angeliki Lazaridou, Anna Potapenko, Olivier Tieleman
We present a method for combining multi-agent communication and traditional data-driven approaches to natural language learning, with an end goal of teaching agents to communicate…
Experience Grounds Language
Yonatan Bisk, Ari Holtzman, Jesse Thomason +9
Language understanding research is held back by a failure to relate language to the physical world it describes and to the social interactions it facilitates. Despite the incredibl…
Shaping representations through communication: community size effect in artificial learning systems
Olivier Tieleman, Angeliki Lazaridou, Shibl Mourad +2
Motivated by theories of language and communication that explain why communities with large numbers of speakers have, on average, simpler languages with more regularity, we cast th…
Biases for Emergent Communication in Multi-agent Reinforcement Learning
Tom Eccles, Yoram Bachrach, Guy Lever +2
We study the problem of emergent communication, in which language arises because speakers and listeners must communicate information in order to solve tasks. In temporally extended…
Learning and Evaluating General Linguistic Intelligence
Dani Yogatama, Cyprien de Masson d'Autume, Jerome Connor +8
We define general linguistic intelligence as the ability to reuse previously acquired knowledge about a language's lexicon, syntax, semantics, and pragmatic conventions to adapt to…
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
Natasha Jaques, Angeliki Lazaridou, Edward Hughes +5
We propose a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning (MARL), through rewarding agents for having causal influence over…
Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input
Angeliki Lazaridou, Karl Moritz Hermann, Karl Tuyls +1
The ability of algorithms to evolve or learn (compositional) communication protocols has traditionally been studied in the language evolution literature through the use of emergent…
Emergent Communication through Negotiation
Kris Cao, Angeliki Lazaridou, Marc Lanctot +3
Multi-agent reinforcement learning offers a way to study how communication could emerge in communities of agents needing to solve specific problems. In this paper, we study the eme…
Compositional Obverter Communication Learning From Raw Visual Input
Edward Choi, Angeliki Lazaridou, Nando de Freitas
One of the distinguishing aspects of human language is its compositionality, which allows us to describe complex environments with limited vocabulary. Previously, it has been shown…
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
Marc Lanctot, Vinicius Zambaldi, Audrunas Gruslys +5
To achieve general intelligence, agents must learn how to interact with others in a shared environment: this is the challenge of multiagent reinforcement learning (MARL). The simpl…
The RepEval 2017 Shared Task: Multi-Genre Natural Language Inference with Sentence Representations
Nikita Nangia, Adina Williams, Angeliki Lazaridou +1
This paper presents the results of the RepEval 2017 Shared Task, which evaluated neural network sentence representation learning models on the Multi-Genre Natural Language Inferenc…
CommAI: Evaluating the first steps towards a useful general AI
Marco Baroni, Armand Joulin, Allan Jabri +4
With machine learning successfully applied to new daunting problems almost every day, general AI starts looking like an attainable goal. However, most current research focuses inst…