191 citations · 280 across the 37 of their papers we have counts for
37 papers
A Representation-Level Assessment of Bias Mitigation in Foundation Models
Svetoslav Nizhnichenkov, Rahul Nair, Elizabeth Daly +1
We investigate how successful bias mitigation reshapes the embedding space of encoder-only and decoder-only foundation models, offering an internal audit of model behaviour through…
PaDeLLM-NER: Parallel Decoding in Large Language Models for Named Entity Recognition
Jinghui Lu, Ziwei Yang, Yanjie Wang +3
In this study, we aim to reduce generation latency for Named Entity Recognition (NER) with Large Language Models (LLMs). The main cause of high latency in LLMs is the sequential de…
Explaining Knock-on Effects of Bias Mitigation
Svetoslav Nizhnichenkov, Rahul Nair, Elizabeth Daly +1
In machine learning systems, bias mitigation approaches aim to make outcomes fairer across privileged and unprivileged groups. Bias mitigation methods work in different ways and ha…
Distance-Aware eXplanation Based Learning
Misgina Tsighe Hagos, Niamh Belton, Kathleen M. Curran +1
eXplanation Based Learning (XBL) is an interactive learning approach that provides a transparent method of training deep learning models by interacting with their explanations. XBL…
Unlearning Spurious Correlations in Chest X-ray Classification
Misgina Tsighe Hagos, Kathleen M. Curran, Brian Mac Namee
Medical image classification models are frequently trained using training datasets derived from multiple data sources. While leveraging multiple data sources is crucial for achievi…
Learning from Exemplary Explanations
Misgina Tsighe Hagos, Kathleen M. Curran, Brian Mac Namee
eXplanation Based Learning (XBL) is a form of Interactive Machine Learning (IML) that provides a model refining approach via user feedback collected on model explanations. Although…