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20242026
most citedTell Me a Story! Narrative-Driven XAI with Large Language Models

4 citations · 4 across the 3 of their papers we have counts for

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cs.CL2026

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…

cs.CL2026

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…

cs.CL2025

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…

cs.CL2025

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…

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…