N\"urnberg NLP @ GermEval Shared Task 2026: Harmful Content Detection in German Social Media through Error-Independent LLM Voters
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Computer Science > Computation and Language
Title:Nürnberg NLP @ GermEval Shared Task 2026: Harmful Content Detection in German Social Media through Error-Independent LLM Voters
Abstract: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.
| Comments: | Accepted at the GermEval 2026 Shared Task on Harmful Content Detection @ KONVENS 2026 (1st place on all four subtasks) |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2608.22246 [cs.CL] |
| (or arXiv:2608.22246v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.22246
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Philipp Steigerwald [view email][v1] Sun, 23 Aug 2026 07:07:06 UTC (253 KB)
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