7 papers · 1 filter
LabelFusion-TS: Fusing Large Language Models, Transformer Encoders, and Financial Time Series for Monetary-Policy Stance Classification
Michael Schlee, Fabian Lukassen, Christoph Weisser
Financial text is produced and interpreted within a market environment, yet financial text classifiers almost always receive text alone. We study whether financial time series are…
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…
LabelFusion: Fusing Large Language Models with Transformer Encoders for Robust Financial News Classification
Michael Schlee, Christoph Weisser, Timo Kivimäki +2
Financial news plays a central role in shaping investor sentiment and short-term dynamics in commodity markets. Many downstream financial applications, such as commodity price pred…
Fine-tuning of lightweight large language models for sentiment classification on heterogeneous financial textual data
Alvaro Paredes Amorin, Andre Python, Christoph Weisser
Large language models (LLMs) play an increasingly important role in financial markets analysis by capturing signals from complex and heterogeneous textual data sources, such as twe…