9 papers
Domain-Adaptation through Synthetic Data: Fine-Tuning Large Language Models for German Law
Ali Hamza Bashir, Muhammad Rehan Khalid, Kostadin Cvejoski +7
Large language models (LLMs) often struggle in specialized domains such as legal reasoning due to limited expert knowledge, resulting in factually incorrect outputs or hallucinatio…
Benchmark Success, Clinical Failure: When Reinforcement Learning Optimizes for Benchmarks, Not Patients
Armin Berger, Manuela Bergau, Helen Schneider +7
Recent Reinforcement Learning (RL) advances for Large Language Models (LLMs) have improved reasoning tasks, yet their resource-constrained application to medical imaging remains un…
History Rhymes: Macro-Contextual Retrieval for Robust Financial Forecasting
Sarthak Khanna, Armin Berger, Muskaan Chopra +2
Financial markets are inherently non-stationary: structural breaks and macroeconomic regime shifts often cause forecasting models to fail when deployed out of distribution (OOD). C…
From Retinal Pixels to Patients: Evolution of Deep Learning Research in Diabetic Retinopathy Screening
Muskaan Chopra, Lorenz Sparrenberg, Armin Berger +3
Diabetic Retinopathy (DR) remains a leading cause of preventable blindness, with early detection critical for reducing vision loss worldwide. Over the past decade, deep learning ha…
Multi-Modal Vision vs. Text-Based Parsing: Benchmarking LLM Strategies for Invoice Processing
David Berghaus, Armin Berger, Lars Hillebrand +2
This paper benchmarks eight multi-modal large language models from three families (GPT-5, Gemini 2.5, and open-source Gemma 3) on three diverse openly available invoice document da…
A Survey on Current Trends and Recent Advances in Text Anonymization
Tobias DeuÃer, Lorenz Sparrenberg, Armin Berger +3
The proliferation of textual data containing sensitive personal information across various domains requires robust anonymization techniques to protect privacy and comply with regul…