4 citations · 6 across the 7 of their papers we have counts for
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cs.CL2024
How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives
Timour Ichmoukhamedov, James Hinns, David Martens
A rapidly developing application of LLMs in XAI is to convert quantitative explanations such as SHAP into user-friendly narratives to explain the decisions made by smaller predicti…
cs.LG2024
GraphXAIN: Narratives to Explain Graph Neural Networks
Mateusz Cedro, David Martens
Graph Neural Networks (GNNs) are a powerful technique for machine learning on graph-structured data, yet they pose challenges in interpretability. Existing GNN explanation methods…
cs.CV2024★ 1 cited
Exposing Image Classifier Shortcuts with Counterfactual Frequency (CoF) Tables
James Hinns, David Martens
The rise of deep learning in image classification has brought unprecedented accuracy but also highlighted a key issue: the use of 'shortcuts' by models. Such shortcuts are easy-to-…