243 citations · 456 across the 8 of their papers we have counts for
10 papers
Solving math word problems with process- and outcome-based feedback
Jonathan Uesato, Nate Kushman, Ramana Kumar +6
Recent work has shown that asking language models to generate reasoning steps improves performance on many reasoning tasks. When moving beyond prompting, this raises the question o…
Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning
Antonia Creswell, Murray Shanahan, Irina Higgins
Large language models (LLMs) have been shown to be capable of impressive few-shot generalisation to new tasks. However, they still tend to perform poorly on multi-step logical reas…
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.…
Unsupervised Object-Based Transition Models for 3D Partially Observable Environments
Antonia Creswell, Rishabh Kabra, Chris Burgess +1
We present a slot-wise, object-based transition model that decomposes a scene into objects, aligns them (with respect to a slot-wise object memory) to maintain a consistent order a…
AlignNet: Unsupervised Entity Alignment
Antonia Creswell, Kyriacos Nikiforou, Oriol Vinyals +8
Recently developed deep learning models are able to learn to segment scenes into component objects without supervision. This opens many new and exciting avenues of research, allowi…
An Explicitly Relational Neural Network Architecture
Murray Shanahan, Kyriacos Nikiforou, Antonia Creswell +3
With a view to bridging the gap between deep learning and symbolic AI, we present a novel end-to-end neural network architecture that learns to form propositional representations w…