activity
20192022
most citedImproving language models by retrieving from trillions of tokens

300 citations · 588 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2022★ 1 cited

Benchmarking Offline Reinforcement Learning Algorithms for E-Commerce Order Fraud Evaluation

Soysal Degirmenci, Chris Jones

Amazon and other e-commerce sites must employ mechanisms to protect their millions of customers from fraud, such as unauthorized use of credit cards. One such mechanism is order fr…

cs.CL2022★ 23 cited

Unified Scaling Laws for Routed Language Models

Aidan Clark, Diego de las Casas, Aurelia Guy +23

The performance of a language model has been shown to be effectively modeled as a power-law in its parameter count. Here we study the scaling behaviors of Routing Networks: archite…

cs.CL2022★ 243 cited

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.…

cs.CL2021★ 300 cited

Improving language models by retrieving from trillions of tokens

Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann +25

We enhance auto-regressive language models by conditioning on document chunks retrieved from a large corpus, based on local similarity with preceding tokens. With a trillion to…

cs.LG2019★ 21 cited

TF-Replicator: Distributed Machine Learning for Researchers

Peter Buchlovsky, David Budden, Dominik Grewe +9

We describe TF-Replicator, a framework for distributed machine learning designed for DeepMind researchers and implemented as an abstraction over TensorFlow. TF-Replicator simplifie…