25 citations · 33 across the 4 of their papers we have counts for
4 papers
You Are What You Write: Preserving Privacy in the Era of Large Language Models
Richard Plant, Valerio Giuffrida, Dimitra Gkatzia
Large scale adoption of large language models has introduced a new era of convenient knowledge transfer for a slew of natural language processing tasks. However, these models also…
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann +122
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained…
CAPE: Context-Aware Private Embeddings for Private Language Learning
Richard Plant, Dimitra Gkatzia, Valerio Giuffrida
Deep learning-based language models have achieved state-of-the-art results in a number of applications including sentiment analysis, topic labelling, intent classification and othe…
CovidTracker: A comprehensive Covid-related social media dataset for NLP tasks
Richard Plant, Amir Hussain
The Covid-19 pandemic presented an unprecedented global public health emergency, and concomitantly an unparalleled opportunity to investigate public responses to adverse social con…