4 citations · 6 across the 9 of their papers we have counts for
3 papers · 1 filter
Why do explanations fail? A typology and discussion on failures in XAI
Clara Bove, Thibault Laugel, Marie-Jeanne Lesot +2
As Machine Learning models achieve unprecedented levels of performance, the XAI domain aims at making these models understandable by presenting end-users with intelligible explanat…
Self-AMPLIFY: Improving Small Language Models with Self Post Hoc Explanations
Milan Bhan, Jean-Noel Vittaut, Nicolas Chesneau +1
Incorporating natural language rationales in the prompt and In-Context Learning (ICL) have led to a significant improvement of Large Language Models (LLMs) performance. However, ge…
Dynamic Interpretability for Model Comparison via Decision Rules
Adam Rida, Marie-Jeanne Lesot, Xavier Renard +1
Explainable AI (XAI) methods have mostly been built to investigate and shed light on single machine learning models and are not designed to capture and explain differences between…