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This paper evaluates retrieval-based in-context learning approaches for detecting criminally relevant hate speech in German social media posts, finding that few-shot prompting outperforms zero-shot but retrieval methods offer marginal gains, with models better suited for triage than autonomous moderation.
The paper presents a nine-voter ensemble system using error-independent LLMs for harmful content detection in German social media, achieving first place in GermEval 2026 shared task across four subtasks by addressing class imbalance.
This paper compares fine-tuned BERT (gbert-large) with few-shot LLM prompting (Llama 4 Maverick) for detecting threat and solution framing in German climate news sentences. BERT achieves higher F1 scores (0.83 vs 0.78), and an ablation study shows that providing preceding sentence context improves performance.
This paper details the RETUYT-INCO team's participation in the BEA 2026 Shared Task 2, introducing a meta-prompting approach for rubric-based scoring of German short answers.