43 citations · 126 across the 10 of their papers we have counts for
11 papers
What is the best recipe for character-level encoder-only modelling?
Kris Cao
This paper aims to benchmark recent progress in language understanding models that output contextualised representations at the character level. Many such modelling architectures a…
Towards Coherent and Consistent Use of Entities in Narrative Generation
Pinelopi Papalampidi, Kris Cao, Tomas Kocisky
Large pre-trained language models (LMs) have demonstrated impressive capabilities in generating long, fluent text; however, there is little to no analysis on their ability to maint…
Control Prefixes for Parameter-Efficient Text Generation
Jordan Clive, Kris Cao, Marek Rei
Prefix-tuning is a powerful lightweight technique for adapting a large pre-trained language model to a downstream application. However, it uses the same dataset-level tuned prompt…
You should evaluate your language model on marginal likelihood over tokenisations
Kris Cao, Laura Rimell
Neural language models typically tokenise input text into sub-word units to achieve an open vocabulary. The standard approach is to use a single canonical tokenisation at both trai…
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…
Game Plan: What AI can do for Football, and What Football can do for AI
Karl Tuyls, Shayegan Omidshafiei, Paul Muller +33
The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball,…