Exploring the Limits of LLMs for German Text Classification: Prompting and Fine-tuning Strategies Across Small and Medium-sized Datasets

Abstract

Large Language Models (LLMs) are highly capable, state-of-the-art technologies and widely used as text classifiers for various NLP tasks, including sentiment analysis, topic classification, legal document analysis, etc. In this paper, we present a systematic analysis of the performance of LLMs as text classifiers using five German datasets from social media across 13 different tasks. We investigate zero- (ZSC) and few-shot classification (FSC) approaches with multiple LLMs and provide a comparative analysis with fine-tuned models based on Llama-3.2, EuroLLM, Teuken and BübleLM. We concentrate on investigating the limits of LLMs and on accurately describing our findings and overall challenges.