N"urnberg NLP @ GermEval Shared Task 2026: Harmful Content Detection in German Social Media through Error-Independent LLM Voters
On the map: LLM safety and security → Safety alignment, refusal and guardrails
Abstract (arXiv)
Harmful content in German social media does real-world damage, from calls to action to criminal defamation. The GermEval 2026 shared task scores its detection in four subtasks. The technical challenge is a severe class imbalance. The harmful classes are rare and share surface language with the dominant majority class, yet under macro-F1 they decide the score. The decisive lever is then not a stronger single model but error independence. This insight becomes a per-subtask nine-voter ensemble spanning three orthogonal axes: LLM, training method and class scope. Selected mainly on internal cross-validation, the system reaches macro-F1 of 89.56 (C2A), 71.63 (DBO), 54.84 (VIO) and 83.02 (DEF) on the hidden test set, placing first on all four subtasks.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Safety alignment, refusal and guardrails, one of 12 directions in LLM safety and security. Builds on 4 of its 38 references that are in the map. First posted on arXiv 23 Aug 2026.
Builds on
4 of 38 references.
References · 4
- 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
- 2024LL"aMmlein: Transparent, Compact and Competitive German-Only Language Models from Scratchcited by 9
- 2026AI Systems in Text-Based Online Counselling: Ethical Considerations Across Three Implementation Approachescited by 4
- 2026N"urnberg NLP at PsyDefDetect: Multi-Axis Voter Ensembles for Psychological Defence Mechanism Classificationcited by 3
Built on by
Citing papers per year: 2026: 1.
1 paper.