1 citations · 1 across the 4 of their papers we have counts for
7 papers
On the Importance and Evaluation of Narrativity in Natural Language AI Explanations
Mateusz Cedro, David Martens
Explainable AI (XAI) aims to make the behaviour of machine learning models interpretable, yet many explanation methods remain difficult to understand. The integration of Natural La…
An Agentic Approach to Generating XAI-Narratives
Yifan He, David Martens
Explainable AI (XAI) research has experienced substantial growth in recent years. Existing XAI methods, however, have been criticized for being technical and expert-oriented, motiv…
Aggregating Local Saliency Maps for Semi-Global Explainable Image Classification
James Hinns, David Martens
Deep learning dominates image classification tasks, yet understanding how models arrive at predictions remains a challenge. Much research focuses on local explanations of individua…
Cash or Comfort? How LLMs Value Your Inconvenience
Mateusz Cedro, Timour Ichmoukhamedov, Sofie Goethals +3
Large Language Models (LLMs) are increasingly proposed as near-autonomous artificial intelligence (AI) agents capable of making everyday decisions on behalf of humans. Although LLM…
Exploring the generalization of LLM truth directions on conversational formats
Timour Ichmoukhamedov, David Martens
Several recent works argue that LLMs have a universal truth direction where true and false statements are linearly separable in the activation space of the model. It has been demon…
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…