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We investigate the zero-shot, few-shot and fine-tuning capabilities of Large Language Models (LLMs) to classify German texts according to the Common European Framework of Reference for Languages (CEFR). Published at the IEEE FLLM 2025 conference.
View on arXivSimulation of electric trucks and charging infrastructure in a logistics network using realistic battery and charging models, driver behavior, and route planning algorithms. Developed in Python with a focus on modularity and extensibility. Project @ Autonomous Intelligent Systems Group.
Research on using Large Language Models (LLMs) for classifying and transferring between German language proficiency levels according to CEFR. Developed an evaluation framework and benchmark for comparative analysis.
View ThesisA comprehensive dataset for German language proficiency classification, containing about 1,500 texts across all CEFR levels (A1-C2). Compiled from multiple sources including the FALKO and MERLIN corpora.
View GitHubA React-based web application to help students at the University of Münster select courses from the "Kerninformatik" area. Features include a visual schedule and automatic conflict detection.
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