2 citations · 3 across the 3 of their papers we have counts for
5 papers
Source Attribution in Retrieval-Augmented Generation
Ikhtiyor Nematov, Tarik Kalai, Elizaveta Kuzmenko +4
While attribution methods, such as Shapley values, are widely used to explain the importance of features or training data in traditional machine learning, their application to Larg…
AIDE: Antithetical, Intent-based, and Diverse Example-Based Explanations
Ikhtiyor Nematov, Dimitris Sacharidis, Tomer Sagi +1
For many use-cases, it is often important to explain the prediction of a black-box model by identifying the most influential training data samples. Existing approaches lack customi…
The Susceptibility of Example-Based Explainability Methods to Class Outliers
Ikhtiyor Nematov, Dimitris Sacharidis, Tomer Sagi +1
This study explores the impact of class outliers on the effectiveness of example-based explainability methods for black-box machine learning models. We reformulate existing explain…
Can Large Language Models Augment a Biomedical Ontology with missing Concepts and Relations?
Antonio Zaitoun, Tomer Sagi, Szymon Wilk +1
Ontologies play a crucial role in organizing and representing knowledge. However, even current ontologies do not encompass all relevant concepts and relationships. Here, we explore…
Hospitalization Length of Stay Prediction using Patient Event Sequences
Emil Riis Hansen, Thomas Dyhre Nielsen, Thomas Mulvad +3
Predicting patients hospital length of stay (LOS) is essential for improving resource allocation and supporting decision-making in healthcare organizations. This paper proposes a n…