4 citations · 7 across the 5 of their papers we have counts for
5 papers
Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models
Alex Havrilla, Andrew Dai, Laura O'Mahony +17
Synthetic data generation with Large Language Models is a promising paradigm for augmenting natural data over a nearly infinite range of tasks. Given this variety, direct compariso…
Towards Utilising a Range of Neural Activations for Comprehending Representational Associations
Laura O'Mahony, Nikola S. Nikolov, David JP O'Sullivan
Recent efforts to understand intermediate representations in deep neural networks have commonly attempted to label individual neurons and combinations of neurons that make up linea…
On the Detection of Anomalous or Out-Of-Distribution Data in Vision Models Using Statistical Techniques
Laura O'Mahony, David JP O'Sullivan, Nikola S. Nikolov
Out-of-distribution data and anomalous inputs are vulnerabilities of machine learning systems today, often causing systems to make incorrect predictions. The diverse range of data…
Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning
Shivalika Singh, Freddie Vargus, Daniel Dsouza +30
Datasets are foundational to many breakthroughs in modern artificial intelligence. Many recent achievements in the space of natural language processing (NLP) can be attributed to t…
Disentangling Neuron Representations with Concept Vectors
Laura O'Mahony, Vincent Andrearczyk, Henning Muller +1
Mechanistic interpretability aims to understand how models store representations by breaking down neural networks into interpretable units. However, the occurrence of polysemantic…