5 papers
CAFE: A Compound-AI Factorial Evaluation Framework
Fabian Lukassen, Christoph Weisser, Thomas Kneib +1
We introduce CAFE (Compound-AI Factorial Evaluation), an open-source platform that brings design of experiments to the evaluation of compound AI systems (CAIS). Such systems expose…
Quality Without Usefulness: LLM-Generated XAI Narratives as Trust Heuristics Rather Than Decision Aids
Fabian Lukassen, Jan Herrmann, Christoph Weisser +3
Prior work shows that Large Language Models (LLMs) can transform Explainable AI (XAI) outputs into Natural Language Explanations (NLEs) that score highly on quality metrics such as…
LLM-Augmented Changepoint Detection: A Framework for Ensemble Detection and Automated Explanation
Fabian Lukassen, Christoph Weisser, Michael Schlee +5
This paper introduces a novel changepoint detection framework that combines ensemble statistical methods with Large Language Models (LLMs) to enhance both detection accuracy and th…
From XAI to Stories: A Factorial Study of LLM-Generated Explanation Quality
Fabian Lukassen, Jan Herrmann, Christoph Weisser +2
Explainable AI (XAI) methods like SHAP and LIME produce numerical feature attributions that remain inaccessible to non expert users. Prior work has shown that Large Language Models…
Hybrid Bernstein Normalizing Flows for Flexible Multivariate Density Regression with Interpretable Marginals
Marcel Arpogaus, Thomas Kneib, Thomas Nagler +1
Density regression models allow a comprehensive understanding of data by modeling the complete conditional probability distribution. While flexible estimation approaches such as no…