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        <title>Exploring the Limits of LLMs for German Text Classification: Prompting and Fine-tuning Strategies Across Small and Medium-sized Datasets</title>
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    <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.</abstract>
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        <date>2025-07</date>
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%0 Journal Article
%T Exploring the Limits of LLMs for German Text Classification: Prompting and Fine-tuning Strategies Across Small and Medium-sized Datasets
%A Leitner, Elena
%A Rehm, Georg
%J Journal for Language Technology and Computational Linguistics
%D 2025
%8 July
%V 38
%N 2
%I German Society for Computational Linguistics and Language Technology
%F leitner-rehm-2025-exploring
%X 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.
%R 10.21248/jlcl.38.2025.277
%U https://aclanthology.org/2025.jlcl-2.1/
%U https://doi.org/10.21248/jlcl.38.2025.277
%P 1-12

Markdown (Informal)

[Exploring the Limits of LLMs for German Text Classification: Prompting and Fine-tuning Strategies Across Small and Medium-sized Datasets](https://aclanthology.org/2025.jlcl-2.1/) (Leitner & Rehm, JLCL 2025)

• Exploring the Limits of LLMs for German Text Classification: Prompting and Fine-tuning Strategies Across Small and Medium-sized Datasets (Leitner & Rehm, JLCL 2025)

ACL

• Elena Leitner and Georg Rehm. 2025. Exploring the Limits of LLMs for German Text Classification: Prompting and Fine-tuning Strategies Across Small and Medium-sized Datasets. Journal for Language Technology and Computational Linguistics, 38(2):1–12.